EURASIP Journal on Image and Video Processing

Impact Factor 1.742

EURASIP Journal on Image and Video Processing Cover Image
Review Article
Open Access

An Overview on Wavelets in Source Coding, Communications, and Networks

EURASIP Journal on Image and Video Processing20072007:060539

https://doi.org/10.1155/2007/60539

©  J. E. Fowler and B. Pesquet-Popescu. 2007

Received: 7 January 2007

Accepted: 11 April 2007

Published: 26 June 2007

Abstract

The use of wavelets in the broad areas of source coding, communications, and networks is surveyed. Specifically, the impact of wavelets and wavelet theory in image coding, video coding, image interpolation, image-adaptive lifting transforms, multiple-description coding, and joint source-channel coding is overviewed. Recent contributions in these areas arising in subsequent papers of the present special issue are described.

[123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214]

Authors’ Affiliations

(1)
Department of Electrical & Computer Engineering, GeoResources Institute, Mississippi State University
(2)
Département Traitement du Signal et des Images, École Nationale Supérieure des Télécommunications

References

  1. Fowler JE: Embedded wavelet-based image compression: state of the art (Eingebettete wavelet-basierte bildkompression: stand der technik). Information Technology 2003,45(5):256-262.View ArticleGoogle Scholar
  2. Fowler JE, Rucker JT: 3D wavelet-based compression of hyperspectral imagery. In Hyperspectral Data Exploitation: Theory and Applications. Edited by: Chang C-I. John Wiley & Sons, Hoboken, NJ, USA; 2007:379-407. chapter 14View ArticleGoogle Scholar
  3. Rucker JT, Fowler JE: Shape-adaptive embedded coding of ocean-temperature imagery. Proceedings of the 40th Asilomar Conference on Signals, Systems, and Computers, October 2006, Pacific Grove, Calif, USA 1887-1891.Google Scholar
  4. Ramchandran K, Vetterli M: Best wavelet packet bases in a rate-distortion sense. IEEE Transactions on Image Processing 1993,2(2):160-175. 10.1109/83.217221View ArticleGoogle Scholar
  5. Bradley JN, Brislawn CM, Hopper T: FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression. Visual Information Processing II, April 1993, Orlando, Fla, USA, Proceedings of SPIE 1961: 293-304.View ArticleGoogle Scholar
  6. Penna B, Tillo T, Magli E, Olmo G: Progressive 3-D coding of hyperspectral images based on JPEG 2000. IEEE Geoscience and Remote Sensing Letters 2006,3(1):125-129. 10.1109/LGRS.2005.859942View ArticleGoogle Scholar
  7. Christophe E, Mailhes C, Duhamel P: Best anisotropic 3-D wavelet decomposition in a rate-distortion sense. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '06), May 2006, Toulouse, France 2: 17-20.Google Scholar
  8. Antonini M, Barlaud M, Mathieu P, Daubechies I: Image coding using wavelet transform. IEEE Transactions of Image Processing 1992,1(2):205-220. 10.1109/83.136597View ArticleGoogle Scholar
  9. Le Gall D, Tabatabai A: Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '88), April 1988, New York, NY, USA 761-764.Google Scholar
  10. Villasenor JD, Belzer B, Liao J: Wavelet filter evaluation for image compression. IEEE Transactions on Image Processing 1995,4(8):1053-1060. 10.1109/83.403412View ArticleGoogle Scholar
  11. Calderbank AR, Daubechies I, Sweldens W, Yeo B-L: Lossless image compression using integer to integer wavelet transforms. Proceedings of IEEE International Conference on Image Processing (ICIP '97), October 1997, Santa Barbara, Calif, USA 1: 596-599.View ArticleGoogle Scholar
  12. Calderbank AR, Daubechies I, Sweldens W, Yeo B-L: Wavelet transforms that map integers to integers. Applied and Computational Harmonic Analysis 1998,5(3):332-369. 10.1006/acha.1997.0238MathSciNetView ArticleMATHGoogle Scholar
  13. Information Technology—JPEG 2000 Image Coding System—Part 1: Core Coding System ISO/IEC 15444-1, 2000Google Scholar
  14. Information Technology—JPEG 2000 Image Coding System—Part 2: Extensions ISO/IEC 15444-2, 2004Google Scholar
  15. Shapiro JM: Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing 1993,41(12):3445-3462. 10.1109/78.258085View ArticleMATHGoogle Scholar
  16. Said A, Pearlman WA: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 1996,6(3):243-250. 10.1109/76.499834View ArticleGoogle Scholar
  17. Islam A, Pearlman WA: Embedded and efficient low-complexity hierarchical image coder. In Visual Communications and Image Processing, January 1999, San Jose, Calif, USA, Proceedings of SPIE Edited by: Aizawa K, Stevenson RL, Zhang Y-Q. 3653: 294-305.Google Scholar
  18. Pearlman WA, Islam A, Nagaraj N, Said A: Efficient, low-complexity image coding with a set-partitioning embedded block coder. IEEE Transactions on Circuits and Systems for Video Technology 2004,14(11):1219-1235. 10.1109/TCSVT.2004.835150View ArticleGoogle Scholar
  19. Fowler JE: Shape-adaptive coding using binary set splitting with k -d trees. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 2: 1301-1304.Google Scholar
  20. Chen Y, Pearlman WA: Three-dimensional subband coding of video using the zero-tree method. In Visual Communications and Image Processing, March 1996, Orlando, Fla, USA, Proceedings of SPIE Edited by: Ansari R, Smith MJT. 2727: 1302-1312.View ArticleGoogle Scholar
  21. Campisi P, Gentile M, Neri A: Three dimensional wavelet based approach for a scalable video conference system. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 3: 802-806.View ArticleGoogle Scholar
  22. Kim B-J, Pearlman WA: An embedded wavelet video coder using three-dimensional set partitioning in hierarchical trees (SPIHT). In Proceedings of Data Compression Conference (DCC '97), March 1997, Snowbird, Utah, USA Edited by: Storer JA, Cohn M. 251-260.Google Scholar
  23. Kim B-J, Xiong Z, Pearlman WA: Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT). IEEE Transactions on Circuits and Systems for Video Technology 2000,10(8):1374-1387. 10.1109/76.889025View ArticleGoogle Scholar
  24. Dragotti PL, Poggi G, Ragozini ARP: Compression of multispectral images by three-dimensional SPIHT algorithm. IEEE Transactions on Geoscience and Remote Sensing 2000,38(1):416-428. 10.1109/36.823937View ArticleGoogle Scholar
  25. He C, Dong J, Zheng YF, Gao Z: Optimal 3-D coefficient tree structure for 3-D wavelet video coding. IEEE Transactions on Circuits and Systems for Video Technology 2003,13(10):961-972. 10.1109/TCSVT.2003.816514View ArticleGoogle Scholar
  26. Cho S, Pearlman WA: Error resilient video coding with improved 3-D SPIHT and error concealment. In Image and Video Communications and Processing, January 2003, Santa Clara, Calif, USA, Proceedings of SPIE Edited by: Vasudev B, Hsing TR, Tescher AG, Ebrahimi T. 5022: 125-136.Google Scholar
  27. Tang X, Cho S, Pearlman WA: 3D set partitioning coding methods in hyperspectral image compression. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 2: 239-242.Google Scholar
  28. Marcellin MW, Bilgin A: Quantifying the parent-child coding gain in zero-tree-based coders. IEEE Signal Processing Letters 2001,8(3):67-69. 10.1109/97.905942View ArticleGoogle Scholar
  29. Tang X, Pearlman WA, Modestino JW: Hyperspectral image compression using three-dimensional wavelet coding. In Image and Video Communications and Processing, January 2003, Santa Clara, Calif, USA, Proceedings of SPIE Edited by: Vasudev B, Hsing TR, Tescher AG, Ebrahimi T. 5022: 1037-1047.Google Scholar
  30. Tang X, Pearlman WA: Three-dimensional wavelet-based compression of hyperspectral images. In Hyperspectral Data Compression. Edited by: Motta G, Rizzo F, Storer JA. Kluwer Academic Publishers, Norwell, Mass, USA; 2006:273-308. chapter 10View ArticleGoogle Scholar
  31. Rucker JT, Fowler JE: Coding of ocean-temperature volumes using binary set splitting with k -d trees. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS '04), September 2004, Anchorage, Alaska, USA 1: 289-292.Google Scholar
  32. Taubman DS, Marcellin MW: JPEG2000: Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, Boston, Mass, USA; 2002.View ArticleGoogle Scholar
  33. Rabbani M, Joshi R: An overview of the JPEG 2000 still image compression standard. Signal Processing: Image Communication 2002,17(1):3-48. 10.1016/S0923-5965(01)00024-8Google Scholar
  34. Skodras A, Christopoulos C, Ebrahimi T: The JPEG 2000 still image compression standard. IEEE Signal Processing Magazine 2001,18(5):36-58. 10.1109/79.952804View ArticleGoogle Scholar
  35. Taubman D: High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing 2000,9(7):1158-1170. 10.1109/83.847830View ArticleGoogle Scholar
  36. Rucker JT, Fowler JE, Younan NH: JPEG2000 coding strategies for hyperspectral data. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS '05), July 2005, Seoul, South Korea 1: 128-131.Google Scholar
  37. Schelkens P, Barbarien J, Cornelis J: Compression of volumetric medical data based on cube-splitting. Applications of Digital Image Processing XXIII, July 2000, San Diego, Calif, USA, Proceedings of SPIE 4115: 91-101.View ArticleGoogle Scholar
  38. Digital Compression and Coding of Continuous-Tone Still Image—Part 1: requirements and guidelines, ISO/IEC 10918-1, 1991Google Scholar
  39. Pennebaker WB, Mitchell JL: JPEG Still Image Compression Standard. Kluwer Academic Publishers, Boston, Mass, USA; 1993.Google Scholar
  40. Fowler JE: QccPack: an open-source software library for quantization, compression, and coding. In Applications of Digital Image Processing XXIII, July-August 2000, San Diego, Calif, USA, Proceedings of SPIE Edited by: Tescher AG. 4115: 294-301.View ArticleGoogle Scholar
  41. Information Technology—Coding of Audio-Visual Objects—Part 2: Visual ISO/IEC 14496-2, 1999, MPEG-4 Coding StandardGoogle Scholar
  42. Li S, Li W: Shape-adaptive discrete wavelet transforms for arbitrarily shaped visual object coding. IEEE Transactions on Circuits and Systems for Video Technology 2000,10(5):725-743. 10.1109/76.856450View ArticleGoogle Scholar
  43. Minami G, Xiong Z, Wang A, Mehrotra S: 3-D wavelet coding of video with arbitrary regions of support. IEEE Transactions on Circuits and Systems for Video Technology 2001,11(9):1063-1068. 10.1109/76.946523View ArticleGoogle Scholar
  44. Lu Z, Pearlman WA: Wavelet coding of video object by object-based SPECK algorithm. Proceedings of the 22nd Picture Coding Symposium (PCS '01), April 2001, Seoul, South Korea 413-416.Google Scholar
  45. Fowler JE: Shape-adaptive tarp coding. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 1: 621-624.Google Scholar
  46. Ziegler G, Lensch HPA, Ahmed N, Magnor M, Seidel H-P: Multi-video compression in texture space. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 4: 2467-2470.Google Scholar
  47. Wang H, Schuster GM, Katsaggelos AK: Rate-distortion optimal bit allocation for object-based video coding. IEEE Transactions on Circuits and Systems for Video Technology 2005,15(9):1113-1123.View ArticleGoogle Scholar
  48. Fowler JE, Fox DN: Embedded wavelet-based coding of three-dimensional oceanographic images with land masses. IEEE Transactions on Geoscience and Remote Sensing 2001,39(2):284-290. 10.1109/36.905236View ArticleGoogle Scholar
  49. Fowler JE, Fox DN: Wavelet-based coding of three-dimensional oceanographic images around land masses. Proceedings of IEEE International Conference on Image Processing (ICIP '00), September 2000, Vancouver, BC, Canada 2: 431-434.Google Scholar
  50. Cagnazzo M, Poggi G, Verdoliva L, Zinicola A: Region-oriented compression of multispectral images by shape-adaptive wavelet transform and SPIHT. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 4: 2459-2462.Google Scholar
  51. Cagnazzo M, Poggi G, Verdoliva L: A comparison of flat and object-based transform coding techniques for the compression of multispectral images. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 1: 657-660.Google Scholar
  52. Penedo M, Pearlman WA, Tahoces PG, Souto M, Vidal JJ: Region-based wavelet coding methods for digital mammography. IEEE Transactions on Medical Imaging 2003,22(10):1288-1296. 10.1109/TMI.2003.817812View ArticleGoogle Scholar
  53. Hua J, Xiong Z, Wu Q, Castleman KR: Fast segmentation and lossy-to-lossless compression of DNA microarray images. Proceedings of the Workshop on Genomic Signal Processing and Statistics (GENSIPS ' 02), October 2002, Raleigh, NC, USAGoogle Scholar
  54. Liu Z, Hua J, Xiong Z, Wu Q, Castleman KR: Lossy-to-lossless ROI coding of chromosome images using modified SPIHT and EBCOT. Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI '02), July 2002, Washington, DC, USA 317-320.Google Scholar
  55. Hua J, Liu Z, Xiong Z, Wu Q, Castleman KR: Microarray BASICA: background adjustment, segmentation, image compression and analysis of microarray images. EURASIP Journal on Applied Signal Processing 2004,2004(1):92-107. 10.1155/S1110865704309200View ArticleMATHGoogle Scholar
  56. Park H-W, Kim H-S: Motion estimation using low-band-shift method for wavelet-based moving-picture coding. IEEE Transactions on Image Processing 2000,9(4):577-587. 10.1109/83.841935View ArticleGoogle Scholar
  57. Chen P, Woods JW: Bidirectional MC-EZBC with lifting implementation. IEEE Transactions on Circuits and Systems for Video Technology 2004,14(10):1183-1194. 10.1109/TCSVT.2004.833165View ArticleGoogle Scholar
  58. Wang B, Wang Y, Selesnick I, Vetro A: Video coding using 3-D dual-tree discrete wavelet transforms. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA 2: 61-64.Google Scholar
  59. Information Technology—Generic Coding of Moving Pictures and Associated Audio Information: Video ISO/IEC 13818-2, MPEG-2 Video Coding Standard, 1995Google Scholar
  60. Advanced Video Coding for Generic Audiovisual Services ITU-T, ITU-T Recommendation H.264, May 2003Google Scholar
  61. Martucci SA, Sodagar I, Chiang T, Zhang Y-Q: A zerotree wavelet video coder. IEEE Transactions on Circuits and Systems for Video Technology 1997,7(1):109-118. 10.1109/76.554422View ArticleGoogle Scholar
  62. van der Auwera G, Munteanu A, Lafruit G, Cornelis J: Video coding based on motion estimation in the wavelet detail images. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '98), May 1998, Seattle, Wash, USA 5: 2801-2804.Google Scholar
  63. Zhang Y-Q, Zafar S: Motion-compensated wavelet transform coding for color video compression. IEEE Transactions on Circuits and Systems for Video Technology 1992,2(3):285-296. 10.1109/76.157160View ArticleGoogle Scholar
  64. Dufaux F, Moccagatta I, Rouchouze B, Ebrahimi T, Kunt M: Motion-compensated generic coding of video based on a multiresolution data structure. Optical Engineering 1993,32(7):1559-1570. 10.1117/12.139807View ArticleGoogle Scholar
  65. Cafforio C, Guaragnella C, Bellifemine F, Chimienti A, Picco R: Motion compensation and multiresolution coding. Signal Processing: Image Communication 1994,6(2):123-142. 10.1016/0923-5965(94)90011-6Google Scholar
  66. Zaciu RC, Bellifemine F: A compression method for image sequences. In Proceedings of IEEE International Conference on Consumer Electronics (ICCE '94), June 1994, Chicago, Ill, USA Edited by: Isnardi MA, Wedam WF. 230-231.View ArticleGoogle Scholar
  67. Zaciu RC, Lamba C, Burlacu C, Nicula G: Image compression using an overcomplete discrete wavelet transform. IEEE Transactions on Consumer Electronics 1996,42(3):800-807. 10.1109/30.536188View ArticleGoogle Scholar
  68. Burrus CS, Gopinath RA, Guo H: Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice Hall, Upper Saddle River, NJ, USA; 1998.Google Scholar
  69. Dutilleux P: An implementation of the "algorithme à trous" to compute the wavelet transform. In Wavelets: Time-Frequency Methods and Phase Space. Edited by: Combes J-M, Grossmann A, Tchamitchian P. Springer, Berlin, Germany; 1989:298-304. Proceedings of the International Conference, Marseille, France, December 1987View ArticleGoogle Scholar
  70. Holschneider M, Kronland-Martinet R, Morlet J, Tchamitchian P: A real-time algorithm for signal analysis with the help of the wavelet transform. In Wavelets: Time-Frequency Methods and Phase Space. Edited by: Combes J-M, Grossmann A, Tchamitchian P. Springer, Berlin, Germany; 1989:286-297. Proceedings of the International Conference, Marseille, France, December 1987View ArticleGoogle Scholar
  71. Shensa MJ: The discrete wavelet transform: wedding the à trous and Mallat algorithms. IEEE Transactions on Signal Processing 1992,40(10):2464-2482. 10.1109/78.157290View ArticleMATHGoogle Scholar
  72. Kim HS, Park HW: Wavelet-based moving-picture coding using shift-invariant motion estimation in wavelet domain. Signal Processing: Image Communication 2001,16(7):669-679. 10.1016/S0923-5965(00)00044-8Google Scholar
  73. Andreopoulos Y, Munteanu A, van der Auwera G, Schelkens P, Cornelius J: Wavelet-based fully-scalable video coding with in-band prediction. Proceedings of the 3rd IEEE Benelux Signal Processing Symposium (SPS '02), March 2002, Leuven, Belgium 217-220.Google Scholar
  74. Li X, Kerofsky L: High performance resolution scalable video coding via all-phase motion compensated prediction of wavelet coefficients. In Visual Communications and Image Processing, January 2002, San Jose, Calif, USA, Proceedings of SPIE Edited by: Kuo C-CJ. 4671: 1080-1090.Google Scholar
  75. Li X, Kerofsky L, Lei S: All-phase motion compensated prediction in the wavelet domain for high performance video coding. Proceedigs of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 3: 538-541.Google Scholar
  76. Li X: Scalable video compression via overcomplete motion compensated wavelet coding. Signal Processing: Image Communication 2004,19(7):637-651. 10.1016/j.image.2004.05.006Google Scholar
  77. Andreopoulos Y, Munteanu A, van der Auwera G, Schelkens P, Cornelis J: Scalable wavelet video-coding with in-band prediction—implementation and experimental results. Proceedings of IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 3: 729-732.View ArticleGoogle Scholar
  78. Andreopoulos Y, Munteanu A, Van der Auwera G, Schelkens P, Cornelis J: A new method for complete-to-overcomplete discrete wavelet transforms. Proceedings of the 14th International Conference on Digital Signal Processing (DSP '02), July 2002, Santorini, Greece 2: 501-504.View ArticleGoogle Scholar
  79. Andreopoulos Y, Munteanu A, Van der Auwera G, Cornelis JPH, Schelkens P: Complete-to-overcomplete discrete wavelet transforms: theory and applications. IEEE Transactions on Signal Processing 2005,53(4):1398-1412.MathSciNetView ArticleGoogle Scholar
  80. Li X: New results of phase shifting in the wavelet space. IEEE Signal Processing Letters 2003,10(7):193-195. 10.1109/LSP.2003.811587View ArticleGoogle Scholar
  81. Cui S, Wang Y, Fowler JE: Mesh-based motion estimation and compensation in the wavelet domain using a redundant transform. Proceedings of IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 1: 693-696.View ArticleGoogle Scholar
  82. Cui S, Wang Y, Fowler JE: Motion estimation and compensation in the redundant-wavelet domain using triangle meshes. Signal Processing: Image Communication 2006,21(7):586-598. 10.1016/j.image.2006.03.011Google Scholar
  83. Cui S, Wang Y, Fowler JE: Multihypothesis motion compensation in the redundant wavelet domain. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 2: 53-56.Google Scholar
  84. Fowler JE, Cui S, Wang Y: Motion compensation via redundant-wavelet multihypothesis. IEEE Transactions on Image Processing 2006,15(10):3102-3113.View ArticleGoogle Scholar
  85. Secker A, Taubman D: Highly scalable video compression using a lifting-based 3D wavelet transform with deformable mesh motion compensation. Proceedings of IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 3: 749-752.View ArticleGoogle Scholar
  86. Taubman D: Successive refinement of video: fundamental issues, past efforts and new directions. In Visual Communications and Image Processing, July 2003, Lugano, Switzerland, Proceedings of SPIE Edited by: Ebrahimi T, Sikora T. 5150: 649-663.Google Scholar
  87. Ohm J-R: Advances in scalable video coding. Proceedings of the IEEE 2005,93(1):42-56.View ArticleGoogle Scholar
  88. Ohm J-R: Three-dimensional subband coding with motion compensation. IEEE Transactions on Image Processing 1994,3(5):559-571. 10.1109/83.334985View ArticleGoogle Scholar
  89. Choi S-J, Woods JW: Motion-compensated 3-D subband coding of video. IEEE Transactions on Image Processing 1999,8(2):155-167. 10.1109/83.743851View ArticleGoogle Scholar
  90. Pesquet-Popescu B, Bottreau V: Three-dimensional lifting schemes for motion compensated video compression. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '01), May 2001, Salt Lake City, Utah, USA 3: 1793-1796.Google Scholar
  91. Secker A, Taubman D: Motion-compensated highly scalable video compression using an adaptive 3D wavelet transform based on lifting. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 2: 1029-1032.Google Scholar
  92. Golwelkar A, Woods JW: Scalable video compression using longer motion compensated temporal filters. In Visual Communications and Image Processing, July 2003, Lugano, Switzerland, Proceedings of SPIE Edited by: Ebrahimi T, Sikora T. 5150: 1406-1416.Google Scholar
  93. Flierl M, Girod B: Video coding with motion-compensated lifted wavelet transforms. Signal Processing: Image Communication 2004,19(7):561-575. 10.1016/j.image.2004.05.002Google Scholar
  94. Bottreau V, Bénetière M, Felts B, Pesquet-Popescu B: A fully scalable 3D subband video codec. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 2: 1017-1020.Google Scholar
  95. Secker A, Taubman D: Lifting-based invertible motion adaptive transform (LIMAT) framework for highly scalable video compression. IEEE Transactions on Image Processing 2003,12(12):1530-1542. 10.1109/TIP.2003.819433View ArticleGoogle Scholar
  96. Luo L, Wu F, Li S, Xiong Z, Zhuang Z: Advanced motion threading for 3D wavelet video coding. Signal Processing: Image Communication 2004,19(7):601-616. 10.1016/j.image.2004.05.004Google Scholar
  97. Turaga DS, van der Schaar M: Wavelet coding for video streaming using new unconstrained motion compensated temporal filtering. Proceedings of International Thyrrhenian Workshop on Digital Communications (IWDC '02), September 2002, Capri, Italy 41-48. Advanced Methods for Multimedia Signal ProcessingGoogle Scholar
  98. Turaga DS, van der Schaar M, Andreopoulos Y, Munteanu A, Schelkens P: Unconstrained motion compensated temporal filtering (UMCTF) for efficient and flexible interframe wavelet video coding. Signal Processing: Image Communication 2005,20(1):1-19. 10.1016/j.image.2004.08.006Google Scholar
  99. Andreopoulos Y, Munteanu A, Barbarien J, van der Schaar M, Cornelis J, Schelkens P: In-band motion compensated temporal filtering. Signal Processing: Image Communication 2004,19(7):653-673. 10.1016/j.image.2004.05.007Google Scholar
  100. Wang Y, Cui S, Fowler JE: 3D video coding using redundant-wavelet multihypothesis and motion-compensated temporal filtering. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 2: 755-758.Google Scholar
  101. Wang Y, Cui S, Fowler JE: 3D video coding with redundant-wavelet multihypothesis. IEEE Transactions on Circuits and Systems for Video Technology 2006,16(2):166-177.View ArticleGoogle Scholar
  102. Tillier C, Pesquet-Popescu B: 3D, 3-band, 3-tap temporal lifting for scalable video coding. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 2: 779-782.Google Scholar
  103. Tillier C, Pesquet-Popescu B, van der Schaar M: 3-band motion-compensated temporal structures for scalable video coding. IEEE Transactions on Image Processing 2006,15(9):2545-2557.View ArticleGoogle Scholar
  104. Trocan M, Tillier C, Pesquet-Popescu B, van der Schaar M: A 5-band temporal lifting scheme for video surveillance. Proceedings of the 8th IEEE Workshop on Multimedia Signal Processing (MMSP '06), October 2006, Victoria, BC, Canada 278-281.Google Scholar
  105. Pau G, Tillier C, Pesquet-Popescu B, Heijmans H: Motion compensation and scalability in lifting-based video coding. Signal Processing: Image Communication 2004,19(7):577-600. 10.1016/j.image.2004.05.003Google Scholar
  106. Chen P, Hanke K, Rusert T, Woods JW: Improvements to the MC-EZBC scalable video coder. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 2: 81-84.Google Scholar
  107. Wu Y, Woods JW: Directional spatial I-blocks for the MC-EZBC video coder. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), May 2004, Montreal, Canada 3: 129-132.Google Scholar
  108. Woods JW, Lilienfield G: A resolution and frame-rate scalable subband/wavelet video coder. IEEE Transactions on Circuits and Systems for Video Technology 2001,11(9):1035-1044. 10.1109/76.946520View ArticleGoogle Scholar
  109. Ye JC, van der Schaar M: Fully scalable 3-D overcomplete wavelet video coding using adaptive motion compensated temporal filtering. In Visual Communications and Image Processing, July 2003, Lugano, Switzerland, Proceedings of SPIE Edited by: Ebrahimi T, Sikora T. 5150: 1169-1180.Google Scholar
  110. Andreopoulos Y, van der Schaar M, Munteanu A, Barbarien J, Schelkens P, Cornelis J: Complete-to-overcomplete discrete wavelet transforms for scalable video coding with MCTF. In Visual Communications and Image Processing, July 2003, Lugano, Switzerland, Proceedings of SPIE Edited by: Ebrahimi T, Sikora T. 5150: 719-731.Google Scholar
  111. Seran V, Kondi LP: 3D based video coding in the overcomplete discrete wavelet transform domain with reduced delay requirements. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 3: 233-236.Google Scholar
  112. Mehrseresht N, Taubman D: A flexible structure for fully scalable motion-compensated 3-D DWT with emphasis on the impact of spatial scalability. IEEE Transactions on Image Processing 2006,15(3):740-753.View ArticleGoogle Scholar
  113. Mehrseresht N, Taubman D: An efficient content-adaptive motion-compensated 3-D DWT with enhanced spatial and temporal scalability. IEEE Transactions on Image Processing 2006,15(6):1397-1412.View ArticleGoogle Scholar
  114. Schwarz H, Marpe D, Wiegand T: Overview of the scalable H.264/MPEG4-AVC extension. Proceedings of IEEE International Conference on Image Processing (ICIP '06), October 2006, Atlanta, Ga, USA 161-164.Google Scholar
  115. Schwarz H, Marpe D, Wiegand T: Overview of the scalable video coding standard. to appear in IEEE Transactions on Circuits and Systems for Video TechnologyGoogle Scholar
  116. Information Technology—JPEG 2000 Image Coding System—Part 3: Motion JPEG 2000, ISO/IEC 15444-3, 2003Google Scholar
  117. André T, Cagnazzo M, Antonini M, Barlaud M: JPEG2000-compatible scalable scheme for wavelet-based video coding. EURASIP Journal on Image and Video Processing 2007, 2007: 11 pages.View ArticleGoogle Scholar
  118. Karlsson G, Vetterli M: Three dimensional sub-band coding of video. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '88), April 1988, New York, NY, USA 2: 1100-1103.Google Scholar
  119. Kingsbury N: Complex wavelets for shift invariant analysis and filtering of signals. Applied and Computational Harmonic Analysis 2001,10(3):234-253. 10.1006/acha.2000.0343MathSciNetView ArticleMATHGoogle Scholar
  120. Reeves TH, Kingsbury N: Overcomplete image coding using iterative projection-based noise shaping. Proceedings of IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 3: 597-600.View ArticleGoogle Scholar
  121. Selesnick IW, Li KY: Video denoising using 2D and 3D dual-tree complex wavelet transforms. Wavelets: Applications in Signal and Image Processing X, August 2003, San Diego, Calif, USA, Proceedings of SPIE 5207: 607-618.Google Scholar
  122. Wang B, Wang Y, Selesnick I, Vetro A: An investigation of 3D dual-tree wavelet transform for video coding. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 2: 1317-1320.Google Scholar
  123. Sweldens W: Lifting scheme: a new philosophy in biorthogonal wavelet constructions. In Wavelet Applications in Signal and Image Processing III, July 1995, San Diego, Calif, USA, Proceedings of SPIE Edited by: Laine AF, Unser MA, Wickerhauser MV. 2569: 68-79.View ArticleGoogle Scholar
  124. Boettcher JB, Fowler JE: Video coding using a complex wavelet transform and set partitioning. IEEE Signal Processing Letters 2007.,14(9):Google Scholar
  125. Desarte P, Macq B, Slock DTM: Signal-adapted multiresolution transform for image coding. IEEE Transactions on Information Theory 1992,38(2, part 2):897-904. 10.1109/18.119749View ArticleGoogle Scholar
  126. Uhl A: Image compression using non-stationary and inhomogeneous multiresolution analyses. Image and Vision Computing 1996,14(5):365-371.View ArticleGoogle Scholar
  127. Sweldens W: The lifting scheme: a construction of second generation wavelets. SIAM Journal on Mathematical Analysis 1998,29(2):511-546. 10.1137/S0036141095289051MathSciNetView ArticleMATHGoogle Scholar
  128. Daubechies I, Sweldens W: Factoring wavelet transforms into lifting steps. Journal of Fourier Analysis and Applications 1998,4(3):247-269. 10.1007/BF02476026MathSciNetView ArticleMATHGoogle Scholar
  129. Sweldens W, Schröder P: Building your own wavelets at home. In Wavelets in Computer Graphics, ACM SIGGRAPH Course Notes. ACM Press, New York, NY, USA; 1996:15-87.Google Scholar
  130. Claypoole R, Davis G, Sweldens W, Baraniuk R: Nonlinear wavelet transforms for image coding. Proceedings of the 31st Asilomar Conference on Signals, Systems & Computers, November 1997, Pacific Grove, Calif, USA 1: 662-667.Google Scholar
  131. Claypoole RL Jr., Davis GM, Sweldens W, Baraniuk RG: Nonlinear wavelet transforms for image coding via lifting. IEEE Transactions on Image Processing 2003,12(12):1449-1459. 10.1109/TIP.2003.817237MathSciNetView ArticleGoogle Scholar
  132. Boulgouris NV, Strintzis MG: Reversible multiresolution image coding based on adaptive lifting. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 3: 546-550.View ArticleGoogle Scholar
  133. Boulgouris NV, Tzovaras D, Strintzis MG: Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding. IEEE Transactions on Image Processing 2001,10(1):1-14. 10.1109/83.892438View ArticleMATHGoogle Scholar
  134. Taubman D: Adaptive, non-separable lifting transforms for image compression. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 3: 772-776.View ArticleGoogle Scholar
  135. Gerek ÖN, Çetin AE: Adaptive polyphase subband decomposition structures for image compression. IEEE Transactions on Image Processing 2000,9(10):1649-1660. 10.1109/83.869176View ArticleGoogle Scholar
  136. Gerek ÖN, Çetin AE: A 2-D orientation-adaptive prediction filter in lifting structures for image coding. IEEE Transactions on Image Processing 2006,15(1):106-111.View ArticleGoogle Scholar
  137. Piella G, Heijmans HJAM: An adaptive update lifting scheme with perfect reconstruction. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 3: 190-193.Google Scholar
  138. Heijmans HJAM, Piella G, Pesquet-Popescu B: Building adaptive 2D wavelet decompositions by update lifting. Proceedings of IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 1: 397-400.View ArticleGoogle Scholar
  139. Piella G, Heijmans HJAM: Adaptive lifting schemes with perfect reconstruction. IEEE Transactions on Signal Processing 2002,50(7):1620-1630. 10.1109/TSP.2002.1011203View ArticleGoogle Scholar
  140. Pesquet-Popescu B, Piella G, Heijmans HJAM: Adaptive update lifting with gradient criteria modeling high-order differences. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 2: 1417-1420.Google Scholar
  141. Piella G, Pesquet-Popescu B, Heijmans HJAM: Adaptive update lifting with a decision rule based on derivative filters. IEEE Signal Processing Letters 2002,9(10):329-332. 10.1109/LSP.2002.804563View ArticleGoogle Scholar
  142. Pesquet-Popescu B, Heijmans HJAM, Abhayaratne GCK, Piella G: Quantization of adaptive 2D wavelet decompositions. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 3: 209-212.Google Scholar
  143. Piella G, Pesquet-Popescu B, Heijmans HJAM: Gradient-driven update lifting for adaptive wavelets. Signal Processing: Image Communication 2005,20(9-10):813-831. 10.1016/j.image.2005.03.016Google Scholar
  144. Piella G, Pesquet-Popescu B, Heijmans HJAM, Pau G: Combining seminorms in adaptive lifting schemes and applications to image analysis and compression. Journal of Mathematical Imaging and Vision 2006,25(2):203-226. 10.1007/s10851-006-6711-yMathSciNetView ArticleGoogle Scholar
  145. Heijmans HJAM, Piella G, Pesquet-Popescu B: Adaptive wavelets for image compression using update lifting: quantization and error analysis. International Journal of Wavelets, Multiresolution and Information Processing 2006,4(1):41-63. 10.1142/S0219691306001087MathSciNetView ArticleMATHGoogle Scholar
  146. Hattay J, Benazza-Benyahia A, Pesquet J-C: Adaptive lifting for multicomponent image coding through quadtree partitioning. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA 2: 213-216.Google Scholar
  147. Gouze A, Antonini M, Barlaud M: Quincunx lifting scheme for lossy image compression. Proceedings of IEEE International Conference on Image Processing (ICIP '00), September 2000, Vancouver, BC, Canada 1: 665-668.Google Scholar
  148. Gouze A, Antonini M, Barlaud M, Macq B: Optimized lifting scheme for two-dimensional quincunx sampling images. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 2: 253-256.Google Scholar
  149. Gouze A, Antonini M, Barlaud M, Macq B: Design of signal-adapted multidimensional lifting scheme for lossy coding. IEEE Transactions on Image Processing 2004,13(12):1589-1603. 10.1109/TIP.2004.837556View ArticleGoogle Scholar
  150. Solé J, Salembier P: Quadratic interpolation and linear lifting design. EURASIP Journal on Image and Video Processing 2007, 2007: 11 pages.View ArticleGoogle Scholar
  151. Muresan DD, Parks TW: Adaptively quadratic (AQua) image interpolation. IEEE Transactions on Image Processing 2004,13(5):690-698. 10.1109/TIP.2004.826097View ArticleGoogle Scholar
  152. Zhu Y, Schwartz SC, Orchard MT: Wavelet domain image interpolation via statistical estimation. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 3: 840-843.Google Scholar
  153. Chang SG, Cvetković G, Vetterli M: Resolution enhancement of images using wavelet transform extrema extrapolation. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '95), May 1995, Detroit, Mich, USA 4: 2379-2382.Google Scholar
  154. Chang SG, Cvetković Z, Vetterli M: Locally adaptive wavelet-based image interpolation. IEEE Transactions on Image Processing 2006,15(6):1471-1485.View ArticleGoogle Scholar
  155. Carey WK, Chuang DB, Hemami SS: Regularity-preserving image interpolation. IEEE Transactions on Image Processing 1999,8(9):1293-1297. 10.1109/83.784441View ArticleGoogle Scholar
  156. Mallat S, Zhong S: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992,14(7):710-732. 10.1109/34.142909View ArticleGoogle Scholar
  157. Combettes PL: The foundations of set theoretic estimation. Proceedings of the IEEE 1993,81(2):182-208.View ArticleGoogle Scholar
  158. Muresan DD, Parks TW: Prediction of image detail. Proceedings of IEEE International Conference on Image Processing (ICIP '00), September 2000, Vancouver, BC, Canada 2: 323-326.Google Scholar
  159. Kinebuchi K, Muresan DD, Parks TW: Image interpolation using wavelet-based hidden Markov trees. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '01), May 2001, Salt Lake, Utah, USA 3: 1957-1960.Google Scholar
  160. Woo DH, Eom IK, Kim YS: Image interpolation based on inter-scale dependency in wavelet domain. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 3: 1687-1690.Google Scholar
  161. Goyal VK: Multiple description coding: compression meets the network. IEEE Signal Processing Magazine 2001,18(5):74-93. 10.1109/79.952806View ArticleGoogle Scholar
  162. Ozarow L: On a source-coding problem with two channels and three receivers. Bell System Technical Journal 1980,59(10):1909-1921.MathSciNetView ArticleMATHGoogle Scholar
  163. Gamal AE, Cover T: Achievable rates for multiple descriptions. IEEE Transactions on Information Theory 1982,28(6):851-857. 10.1109/TIT.1982.1056588View ArticleMATHGoogle Scholar
  164. Venkataramani R, Kramer G, Goyal VK: Multiple description coding with many channels. IEEE Transactions on Information Theory 2003,49(9):2106-2114. 10.1109/TIT.2003.815767MathSciNetView ArticleMATHGoogle Scholar
  165. Apostolopoulos JG: Reliable video communication over lossy packet networks using multiple state encoding and path diversity. In Visual Communications and Image Processing, January 2001, San Jose, Calif, USA, Proceedings of SPIE Edited by: Girod B, Bouman CA, Steinbach EG. 4310: 392-409.Google Scholar
  166. Vaishampayan VA: Design of multiple description scalar quantizers. IEEE Transactions on Information Theory 1993,39(3):821-834. 10.1109/18.256491View ArticleMATHGoogle Scholar
  167. Vaishampayan VA, Domaszewicz J: Design of entropy-constrained multiple-description scalar quantizers. IEEE Transactions on Information Theory 1994,40(1):245-250. 10.1109/18.272491View ArticleMATHGoogle Scholar
  168. Servetto SD, Ramchandran K, Vaishampayan VA, Nahrstedt K: Multiple description wavelet based image coding. IEEE Transactions on Image Processing 2000,9(5):813-826. 10.1109/83.841528View ArticleGoogle Scholar
  169. Jiang W, Ortega A: Multiple description coding via polyphase transform and selective quantization. In Visual Communications and Image Processing, January 1999, San Jose, Calif, USA, Proceedings of SPIE Edited by: Aizawa K, Stevenson RL, Zhang Y-Q. 3653: 998-1008.View ArticleGoogle Scholar
  170. Miguel AC, Mohr AE, Riskin EA: SPIHT for generalized multiple description coding. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 3: 842-846.View ArticleGoogle Scholar
  171. Wang Y, Orchard MT, Reibman AR: Multiple description image coding for noisy channels by pairing transform coefficients. Proceedings of the 1st IEEE Workshop on Multimedia Signal Processing, June 1997, Princeton, NJ, USA 419-424.Google Scholar
  172. Wang Y, Orchard MT, Reibman AR: Optimal pairwise correlating transforms for multiple description coding. Proceedings of IEEE International Conference on Image Processing (ICIP '98), October 1998, Chicago, Ill, USA 1: 679-683.Google Scholar
  173. Wang Y, Orchard MT, Vaishampayan V, Reibman AR: Multiple description coding using pairwise correlating transforms. IEEE Transactions on Image Processing 2001,10(3):351-366. 10.1109/83.908500View ArticleMATHGoogle Scholar
  174. Goyal VK, Kovačević J: Optimal multiple description transform coding of Gaussian vectors. In Proceedings of IEEE Data Compression Conference (DCC '98), March-April 1998, Snowbird, Utah, USA Edited by: Storer JA, Cohn M. 388-397.Google Scholar
  175. Goyal VK, Kovačević J: Generalized multiple description coding with correlating transforms. IEEE Transactions on Information Theory 2001,47(6):2199-2224. 10.1109/18.945243View ArticleMATHGoogle Scholar
  176. Yang X, Ramchandran K: Optimal multiple description subband coding. Proceedings of IEEE International Conference on Image Processing (ICIP '98), October 1998, Chicago, Ill, USA 1: 654-658.Google Scholar
  177. Yang X, Ramchandran K: Optimal subband filter banks for multiple description coding. IEEE Transactions on Information Theory 2000,46(7):2477-2490. 10.1109/18.887859MathSciNetView ArticleMATHGoogle Scholar
  178. Goyal VK, Kovačević J, Vetterli M: Quantized frame expansions as source-channel codes for erasure channels. In Proceedings of IEEE Data Compression Conference (DCC '99), March 1999, Snowbird, Utah, USA Edited by: Storer JA, Cohn M. 326-335.Google Scholar
  179. Goyal VK, Kovačević J, Kelner JA: Quantized frame expansions with erasures. Applied and Computational Harmonic Analysis 2001,10(3):203-233. 10.1006/acha.2000.0340MathSciNetView ArticleMATHGoogle Scholar
  180. Kovačević J, Dragotti PL, Goyal VK: Filter bank frame expansions with erasures. IEEE Transactions on Information Theory 2002,48(6):1439-1450. 10.1109/TIT.2002.1003832View ArticleMATHGoogle Scholar
  181. Servetto SD, Vaishampayan VA, Sloane NJA: Multiple description lattice vector quantization. In Proceedings of IEEE Data Compression Conference (DCC '99), March 1999, Snowbird, Utah, USA Edited by: Storer JA, Cohn M. 13-22.Google Scholar
  182. Vaishampayan VA, Sloane NJA, Servetto SD: Multiple-description vector quantization with lattice codebooks: design and analysis. IEEE Transactions on Information Theory 2001,47(5):1718-1734. 10.1109/18.930913MathSciNetView ArticleMATHGoogle Scholar
  183. Kelner JA, Goyal VK, Kovačević J: Multiple description lattice vector quantization: variations and extensions. In Proceedings of IEEE Data Compression Conference (DCC '00), March 2000, Snowbird, Utah, USA Edited by: Storer JA, Cohn M. 480-489.View ArticleGoogle Scholar
  184. Goyal VK, Kelner JA, Kovačević J: Multiple description vector quantization with a coarse lattice. IEEE Transactions on Information Theory 2002,48(3):781-788. 10.1109/18.986048View ArticleMATHGoogle Scholar
  185. Berger T: Rate Distortion Theory. Prentice-Hall, Englewood Cliffs, NJ, USA; 1971.Google Scholar
  186. Cover TM, Thomas JA: Elements of Information Theory. 2nd edition. John Wiley & Sons, Hoboken, NJ, USA; 2006.MATHGoogle Scholar
  187. Pradhan SS, Ramchandran K: On the optimality of block orthogonal transforms for multiple description coding of Gaussian vector sources. IEEE Signal Processing Letters 2000,7(4):76-78. 10.1109/97.833002View ArticleGoogle Scholar
  188. Goyal VK, Vetterli M, Thao NT: Quantized overcomplete expansions in ℝ N : analysis, synthesis, and algorithms. IEEE Transactions on Information Theory 1998,44(1):16-31. 10.1109/18.650985MathSciNetView ArticleMATHGoogle Scholar
  189. Lee WS, Pickering MR, Frater MR, Arnold JF: A robust codec for transmission of very low bit-rate video over channels with bursty errors. IEEE Transactions on Circuits and Systems for Video Technology 2000,10(8):1403-1412. 10.1109/76.889033View ArticleGoogle Scholar
  190. Reibman AR, Jafarkhani H, Wang Y, Orchard MT, Puri R: Multiple-description video coding using motion-compensated temporal prediction. IEEE Transactions on Circuits and Systems for Video Technology 2002,12(3):193-204. 10.1109/76.993440View ArticleGoogle Scholar
  191. Bajic IV, Woods JW: Domain-based multiple description coding of images and video. IEEE Transactions on Image Processing 2003,12(10):1211-1225. 10.1109/TIP.2003.817248View ArticleGoogle Scholar
  192. Franchi N, Fumagalli M, Lancini R, Tubaro S: Multiple description video coding for scalable and robust transmission over IP. IEEE Transactions on Circuits and Systems for Video Technology 2005,15(3):321-334.View ArticleGoogle Scholar
  193. Wang Y, Reibman AR, Lin S: Multiple description coding for video delivery. Proceedings of the IEEE 2005,93(1):57-70.View ArticleGoogle Scholar
  194. Vaishampayan VA, John S: Balanced interframe multiple description video compression. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 3: 812-816.View ArticleGoogle Scholar
  195. Wang Y, Lin S: Error-resilient video coding using multiple description motion compensation. IEEE Transactions on Circuits and Systems for Video Technology 2002,12(6):438-452. 10.1109/TCSVT.2002.800320View ArticleGoogle Scholar
  196. van der Schaar M, Turaga DS: Multiple description scalable coding using wavelet-based motion compensated temporal filtering. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 3: 489-492.Google Scholar
  197. Tillier C, Pesquet-Popescu B, van der Schaar M: Multiple descriptions scalable video coding. Proceedings of the 12th European Signal Processing Conference (EUSIPCO '04), September 2004, Vienna, AustriaGoogle Scholar
  198. Kim J, Mersereau RM, Altunbasak Y: Network-adaptive video streaming using multiple description coding and path diversity. Proceedings of IEEE International Conference on Multimedia and Expo (ICME '03), July 2003, Baltimore, Md, USA 2: 653-656.Google Scholar
  199. Franchi N, Fumagalli M, Gatti G, Lancini R: A novel error-resilience scheme for a 3-D multiple description video coder. Proceedings of the Picture Coding Symposium (PSC '04), December 2004, San Francisco, Calif, USA 373-376.Google Scholar
  200. Cho S, Pearlman WA: Error resilient compression and transmission of scalable video. In Applications of Digital Image Processing XXIII, July 2000, San Diego, Calif, USA, Proceedings of SPIE Edited by: Tescher AG. 4115: 396-405.View ArticleGoogle Scholar
  201. Shannon CE: Coding theorems for a discrete source with a fidelity criterion. IRE International Convention Record 1959, 7, part 4: 142-163.Google Scholar
  202. Srinivasan M, Chellappa R: Adaptive source-channel subband video coding for wireless channels. IEEE Journal on Selected Areas in Communications 1998,16(9):1830-1839. 10.1109/49.737651View ArticleGoogle Scholar
  203. Hagenauer J: Rate-compatible punctured convolutional codes (RCPC codes) and their applications. IEEE Transactions on Communications 1988,36(4):389-400. 10.1109/26.2763View ArticleGoogle Scholar
  204. Cheung G, Zakhor A: Joint source/channel coding of scalable video over noisy channels. Proceedings of IEEE International Conference on Image Processing (ICIP '96), September 1996, Lausanne, Switzerland 3: 767-770.View ArticleGoogle Scholar
  205. Cheung G, Zakhor A: Bit allocation for joint source/channel coding of scalable video. IEEE Transactions on Image Processing 2000,9(3):340-356. 10.1109/83.826773View ArticleGoogle Scholar
  206. Shoham Y, Gersho A: Efficient bit allocation for an arbitrary set of quantizers. IEEE Transactions on Acoustics, Speech, and Signal Processing 1988,36(9):1445-1453. 10.1109/29.90373View ArticleMATHGoogle Scholar
  207. Bystrom M, Stockhammer T: Dependent source and channel rate allocation for video transmission. IEEE Transactions on Wireless Communications 2004,3(1):258-268. 10.1109/TWC.2003.821150View ArticleGoogle Scholar
  208. Hagenauer J, Stockhammer T: Channel coding and transmission aspects for wireless multimedia. Proceedings of the IEEE 1999,87(10):1764-1777. 10.1109/5.790636View ArticleGoogle Scholar
  209. Xiong Z, Kim B-J, Pearlman WA: Progressive video coding for noisy channels. Proceedings of IEEE International Conference on Image Processing (ICIP '98), October 1998, Chicago, Ill, USA 1: 334-337.Google Scholar
  210. Cho S, Pearlman WA: A full-featured, error-resilient, scalable wavelet video codec based on the set partitioning in hierarchical trees (SPIHT) algorithm. IEEE Transactions on Circuits and Systems for Video Technology 2002,12(3):157-171. 10.1109/76.993437View ArticleGoogle Scholar
  211. Zhang Z, Liu G, Yang Y: Progressive source-channel coding of video for unknown noisy channels. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 3: 2493-2496.Google Scholar
  212. Gallager RG: Low-density parity-check codes. IRE Transacations on Information Theory 1962,8(1):21-28. 10.1109/TIT.1962.1057683MathSciNetView ArticleMATHGoogle Scholar
  213. MacKay DJC: Good error-correcting codes based on very sparse matrices. IEEE Transactions on Information Theory 1999,45(2):399-431. 10.1109/18.748992MathSciNetView ArticleMATHGoogle Scholar
  214. Farvardin N, Vaishampayan V: Optimal quantizer design for noisy channels: an approach to combined source-channel coding. IEEE Transactions on Information Theory 1987,33(6):827-838. 10.1109/TIT.1987.1057373MathSciNetView ArticleMATHGoogle Scholar

Copyright

© J. E. Fowler and B. Pesquet-Popescu. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.