- Open Access
Power-optimized log-based image processing system
© Dhandapani and Ramachandran; licensee Springer. 2014
- Received: 21 March 2014
- Accepted: 4 July 2014
- Published: 21 July 2014
The continuous development of devices such as mobile phones and digital cameras has led to a higher amount of research being dedicated to the image processing field. Today's image-acquiring tools require battery-operated power, and hence, power optimization becomes a major factor to be considered in the hardware implementation of image systems. This paper proposes an image processing system which utilizes set partitioning in hierarchical trees (SPIHT)-integrated discrete wavelet transform (DWT) structure for image processing. The overall advantage of this proposal is achieved by modifying the arithmetic units in the DWT structure. Utilizing a logarithm-based floating point unit (FPU) in the DWT computation structures, the logarithmic number system (LNS) adaptation in the arithmetic unit results in overall accuracy enhancement with reduced area and power consumption. To ensure the versatility of the proposal and for further evaluating the performance and correctness of the structure, the model is implemented using Xilinx and Altera field-programmable gate array (FPGA) devices. The analyses obtained from the implementation show that the structure incorporated with the log-based FPU is 25% more accurate with 47% reduced power consumption than the integer-styled FPU incorporated DWTs, along with enhanced speed and optimal area utilization.
- Discrete wavelet transform (DWT)
- Lifting scheme
- Log principles
- Floating point unit (FPUs)
- Set partition in hierarchical trees (SPIHT)
- Image coding
- Field-programmable gate array (FPGA) implementation
- Real-time processing
Discrete wavelet transform (DWT) is increasingly being used for image coding. In particular, biorthogonal symmetric wavelets manifested remarkable abilities in still image compression. Hence, this paper proposes an image processing system by focusing on the biorthogonal 9/7 DWT structure. DWT has traditionally been implemented using the convolution method. This implementation demands a large number of computations and storage features that are not desirable for high-speed or low-power applications. Swelden  proposed a new mathematical formulation for wavelet transformation based on spatial construction of the wavelets, and a very versatile scheme for its factorization has been suggested in . This approach is called the lifting-based wavelet transform. The main feature of the lifting-based DWT scheme is to break up high-pass and low-pass filters into a sequence of upper and lower triangular matrices and convert the filter implementation into banded matrix multiplications. This scheme has several advantages when compared to the convolution techniques, which includes ‘in-place’ computation of the DWT, symmetric forward, and inverse transform. Therefore, the DWT implemented using the lifting scheme in the JPEG 2000 standard are the biorthogonal lossless 5/3 integer and the lossy 9/7 floating point filter banks. Numerous architectures have been proposed in order to provide low-power, high-speed, and area-efficient hardware implementation for DWT computation [3–16]. Shi et al.  proposed efficient folded architecture (EFA) with low hardware complexity. The flipping structure is another important DWT architecture that was proposed by Huang et al. . A high-speed, reduced-area two-dimensional (2-D) DWT architecture was proposed by Zhang et al. . While most of these architectures are related to research involved in the optimization of critical paths, only some of them, such as Lee et al. , deal not only with the internal data path but also with the coefficient precision optimization.
This paper focuses on lossy biorthogonal 9/7 lifting-based DWT. This yields higher computational complexity with floating point computations. The implementation of this structure in hardware requires an additional complex hardware to handle the floating point computations. This demands a separate unit for its processing, which leads to the design of the floating point unit (FPU). By exploring the existing FPUs, the phenomenon of arithmetic computations are still the same as ordinary arithmetic logic unit (ALU) operations, acting like an additional prop up for normal ALUs. An island-style with embedded FPU  is proposed by Beauchamp et al., while a coarse-grained FPU was suggested by Ho et al. . Even et al.  suggests a multiplier for performing on either single-precision or double-precision floating point numbers. An optimized FPU in a hybrid FPGA was suggested by Yu et al.  and a configurable multimode FPU for FPGAs by Chong and Parameswaran . Performance improvisation and optimization of these suggested models are studied and employed in each successive development time frame. However, while these models fine tune the FPU in terms of area, there were no suggestions for power reduction or accuracy enhancements. Anand et al.  proposed a log lookup table (LUT)-based FPU, which utilizes a logarithmic principle to achieve good accuracy with reduced power consumption. However, this model has some serious drawbacks, which include increased delay and additional memory for the log LUT handling. The above factors affect the performance in terms of area and speed. Hence, this proposed scheme suggests an efficient model for performing floating point operations to reduce power consumption by reducing the operation complexities using log conversion .This reduces the overall computation burden, as the process is simply a numerical transformation to the logarithmic domain. Thus, a reduction in power consumption and increased accuracy is attained with optimal area usage . The mere mapping of floating point numerals is not possible, and hence, a standardized form is adopted by using IEEE 754 single-precision floating point standard . An optimized DWT architecture with log-based FPU is proposed, and a preliminary version of this work was presented in . This paper revises the external memory access, and a more accurate and detailed error analysis and the simulation results are given.
After the lifting-based DWT was introduced, several coding algorithms were proposed to code the wavelet coefficients into an efficient result, while taking storage space and redundancy into consideration. These algorithms are embedded zerotree wavelet (EZW), embedded block coding with optimized truncation (EBCOT), and set partitioning in hierarchical trees (SPIHT). Among these, the SPIHT algorithm is most preferable because of its low-computational complexity and better image compression performance. The SPIHT coding, proposed by Said and Pearlman in 1996 , does not required arithmetic coding and provides a cheaper and faster hardware solution. It was modified by Wheeler and Pearlman  by making a no list SPIHT (NLS) to reduce memory usage. Later, Corsonello et al. proposed a low-cost implementation of NLS in order to improve the coding speed. The work in  modified the scanning process and utilized fixed memory allocation for the data list to reduce the hardware complexity. In order to achieve high throughput, Cheng et al.  proposed a modified SPIHT that processes a 4 × 4 bit plane in 1 cycle. Fry and Hauck  improvised this model with a bit plane parallel SPIHT encoder architecture to further increase the throughput. By the year 2013, Jin and Lee  proposed a block-based pass-parallel SPIHT (BPS) algorithm, which employs pipelining and parallelism. This scheme has the highest throughput among the existing architectures. Hence, we espouse the BPS in our image processing core.
This proposal introduces an enhanced image processing system, which utilizes a low-power DWT structure along with a log-based FPU and BPS coder. The optimized decomposition level of DWT is selected based on performance parameters such as peak signal-to-noise ratio, compression ratio, and computational complexity. To examine the specific hardware performance and trade-offs associated with the solutions presented here, the architecture is first verified in Matlab for the image parameters. In addition to this, the hardware implementation is carried out using Verilog hardware description language (HDL) and synthesized using Xilinx and Altera FPGA families to verify its device level performance based on VLSI parameters.
The rest of the paper flow is given in brief as follows. Section 2 gives the background supporting the basic understanding of lifting-based discrete wavelet transform and SPIHT coding techniques. Section 3 pursues with the hardware implementation of forward 2-D DWT with modified computation unit adopting log-based FPU and SPIHT coders. Detailed experimental setup for the proposed real-time image processing system and the performance of the proposed architecture is assessed and compared with that of other existing architectures are given in Section 4. Conclusion and final remarks are given in Section 5.
2.1 Discrete wavelet transform
2.1.1 Lifting scheme
- 1.Spliting. The original signal X(n) is split into odd and even sequences (lazy wavelet transform)(4)(5)
Lifting. It consists of one or more steps m of the form
- (a)Predict/Dual lifting. If X(n) possesses local correlation, then X e (n) and X o (n) also have local correlation. Therefore, one subset is used to predict the other subset. In the prediction step, the filtered even array is used to predict the odd array. The new odd array is redefined as the difference between the existing array and the predicted one.(6)
- (b)Update/Primal lifting. To eliminate aliasing which appears due to the down sampling of the original signal, the even array is updated using the filtered new odd array.(7)
Normalization/Scaling. After m lifting steps, scaling coefficients K and 1/K are applied respectively to the even and odd samples in order to obtain the low-pass subband and high-pass subband.
For the biorthogonal 9/7 wavelet, four lifting steps and one scaling can be used, where s1(z) = α(1 + z-1), s2(z) = γ(1 + z-1), t1(z) = β(1 + z), and t2(z) = δ(1 + z). The parameters α, β, γ, and δ are two-tap symmetric filter coefficients and K and 1/K are scaling factors.
where α = -1.586134342, β = -0.05298011854, γ = 0.8829110762, δ = 0.4435068522, and K = 1.149604398
The original data to be filtered is denoted by X(n), and the outputs are a i and d i which are the approximation coefficients and detail coefficients, respectively. We focus on the implementation issue of the lifting-based DWT, which yields higher computational complexity with floating point computation. Hence, we suggest an efficient model for performing the floating point operation to reduce the power by reducing the operating complexities by adopting log conversion [22, 23].
2.2 Set partition in hierarchical trees
Note: w(i, j) is the coefficient value for (i, j) position in the wavelet domain. T stands for the set of coefficients and S n (T) is used for significant state of T at bit plane n.
When S n (T) is ‘0’ , T is called an insignificant set. Otherwise, T is called a significant set. An insignificant set can be represented as a single bit ‘0’. The significant set is partitioned into subsets, and its significances have to be tested again based on the zerotree hypotheses. The SPIHT encodes a given set T and its descendants (denoted by D(T)) together by checking the significance of T ∪ D(T) and by representing T ∪ D(T) as a single symbol ‘0’ if T ∪ D(T) is insignificant. On the other hand, if T ∪ D(T) is significant, T has to partitioned into subsets and each subset is tested independently.
The spatial orientation trees are illustrated in Figure 2b for a 16 × 16 image and is transformed by three levels of discrete wavelet decomposition. Each level is divided into four subbands. The subband a2a2 is divided into four groups of 2 × 2 coefficients. In each group, each of the four coefficients becomes the root of a spatial orientation tree. The square denoted by R in Figure 2a represents the subband a3a3 (low pass subband) in Figure 2b, which corresponds to the root. In order to increase the speed of both the encoder and decoder, we adopt a BPS algorithm  for our image processing core. BPS algorithm modifies the processing order of the original SPIHT algorithm so that an image is partitioned into multiblocks, and the coefficients trees are local to these blocks. Furthermore, BPS employs pipelining and parallelism, which gives the highest throughput among the existing architectures.
3.1 Discrete wavelet transform core
In hardware implementation, the multiplier occupies a large amount of hardware resources. In order to provide a low-power, high-speed, and area-efficient multiplier for DWT computation, Shi et al.  adopted the shift-add operations to optimize the multiplications since the coefficients of wavelet filters are constant. Zhang et al.  used the dedicated 18-bit multiplier block present in the FPGA. In spite of the numerous methods that were proposed, the overall latency in the circuit also depended on the multiplier. Hence, it is necessary to modify the multiplier structure in order to achieve minimum area and computation time. Furthermore, the accuracy also depends on floating point lifting coefficients and its arithmetic operations. The above three factors demand modification of computation units in the DWT architecture. Hence, this proposes a new computational unit based on logarithmic principle in order to achieve minimal computation time with optimal area consumption. Moreover, adaptation of the log principle results in good power reduction mainly because of reduced operator and operand strengths. In the next subsection, log-based floating point unit is discussed.
Log-based floating point unit
The log-based arithmetic unit embedded in the designed FPU utilizes the carry save adder for computing all arithmetic operations. It uses simple log principles, along with operational switches, to select the inputs based on the operation needs. If the adder operator is fed to the switch, the addition computation phenomenon is carried out by merely adding or subtracting the mantissa bits according to the exponent and sign bits. The difference of the two exponents is calculated. If any, perform the mantissa shift and set the larger exponent as the tentative exponent of the result. Shift the mantissa of the smaller exponent to the right by the difference in the exponents. According to the sign bit, perform addition (if equal) or subtraction (if unequal) on the mantissas to get the tentative mantissa as the result. Normalize and round off the mantissa result. If there is an overflow due to rounding, shift right and increment the exponent by 1 bit. Have the highest of the sign bits be the sign bit of the result. Similarly, a multiplication computation procedure is chosen for multiplier input that is fed to the operator switch. The overall data path involved in the multiplier component of this FPU architecture gets simplified. This is a mere computation with only mapping involved. Hence, this simplifies the overall stages involved in multiplications. The mantissas of the input data are mapped to the corresponding logarithmic number in the LUT. This is followed by adding the logarithms. If any overflow shifts the result to the right, then map with antilogarithm LUT to obtain the mantissa of the result. The exponent of the result is obtained by mere addition of the exponent bits, and the sign bit of the result is obtained by the Ex-or-ing both sign bits.
3.2 Block-based parallel-pipelined SPIHT
The overall performance of the proposed image processing system is analyzed in this section. As DWT has a wide range of applications in various fields, the proposed system utilizes its efficiency for enhanced image handling and offers good improvement in speed and area consumption. Moreover, the accuracy of the output is also dealt with by modifying the computation parts in the DWT structure. This utilizes logarithmic principle and, hence, yields a good reduction in power. Furthermore, at each level of DWT, precision also depends on decomposition at that stage. Hence, it is necessary to select an optimized level of DWT. During the experimentation of this proposal, the optimized level of DWT is selected based on performance parameters such as peak signal-to-noise ratio (PSNR), compression ratio (CR), and wavelet decomposition computation complexity. The architecture is first verified using Matlab for the image parameters and then implemented in hardware to analyze its hardware efficiency.
4.1 Image parameters analysis
PSNR values for different decomposition levels
PSNR value for different decomposition levels
where X(i, j) represents the original N × N image and Y(i, j) represents the reconstructed image.
4.2 Numerical accuracy analysis
This work is also concerned with precisions, which is the most important factor of this design. As B9/7 DWT structure utilizes floating point coefficients, accuracy in the result mainly depends on the fractional computational values. Hence, the results obtained with normal integer computation units in DWT suffer from poor accuracy. Moreover, the addition of floating point operation units increases the accuracy. On the other hand, it also increases area and delay overhead. Hence, a logarithm-based FPU is integrated along with the DWT structure to achieve a good reduction in area with a higher improvement in accuracy. As the whole model depends on the log values, the accuracy of the log values is directly related to the accuracy of the result. Furthermore, as std. single precision IEEE754 has 23 mantissa bits, the accuracy also depends on the correctness of the bits. So, in the experimental phase, the analysis of the accuracy is done by two means: output accuracy and bit level accuracy. As accuracy is mostly discussed in its contrary term, the error rate is taken into consideration when discussing accuracy.
where, t is the number of mantissa bits.
Output accuracy percentage computation
Number of output bits
Percent of error
Wallace tree multiplier
Logarithm word size in bits
Output bit error rate computation
Number of output bits
Average bit change error (for 215test sets)
Wallace tree multiplier
Logarithm based multiplier
Logarithm word size in bits
4.3 Hardware analysis
The log-based floating point computation achieves superior accuracy when compared with normal floating point arithmetic computation. Hence, the computation unit based on the log principle is appended with the biorthogonal DWT structure, which is then implemented in FPGA to analyze its performance in hardware. The analyses were done in two different FPGA environments to show the versatility of the proposed idea as there was no inbuilt IPs used.
4.3.1 Hardware result analysis based on Xilinx device
Hardware utilization comparison
Virtex 6 FPGA parameters
Integer multiplier-based DWT
Log multiplier-based DWT
4.3.2 Hardware result analysis based on Altera device
Altera level analysis
Altera family and devices
Cyclone IV E
DWT with BPS
Sync signal specification
Resets and Initialize memory
Sync to Address controller
Sync to R/W control
Resets DWT-SPIHT and switch to Input mode
Activates DWT. Sequentially control R/W sync and initiates SPIHT
Resets IDWT-ISPIHT and switch to input mode
Activates ISPIHT. Sequentially control R/W sync and initiates IDWT
Stand by VGA process
Activates VGA Process and VGA CLK
This paper has proposed an enhanced image processing system utilizing DWT structure with log-based floating point computation units and SPIHT coders. Hence, efficient decomposition levels of DWT and SPIHT algorithms have to be chosen for the hardware implementation. From the detailed analysis performed with various test images, it is found that the five-level decompositions in DWT and block-based parallel-pipelined SPIHT give a good PSNR value irrespective of the compression ratio. This paper adopted a modified arithmetic unit in the DWT structure to achieve good accuracy with minimum latency and power. The modification is stated for the computation units in the DWT structure which are merely integer-styled operation units. As floating point operations are much more complex than integer-based operations, the complexity of the computation hardware also increases. This results in the degradation of the efficiency of DWT operations. Hence, this paper introduced a log-based computation structure to minimize the strength of the operations. Furthermore, it is also found from the results that the accuracy of DWT gets increased as the rounding off errors are fewer with log transformations. The overall structure got 25% improvement in accuracy with the proposed log-based FPUs. In addition, the utilization of LNS in the model provides 47% power reduction in the structure as the overall signal activity and strength is reduced. Hence, the proposed structure features high speed, good accuracy, and low-power utilization. Thus, the adaptation of this structure in the proposed image processing system results in good hardware optimization. Moreover, the model was tested in different environments to test its robustness and versatility. This was done by implementing the model in different FPGAs. This shows that the model is best suited for portable image analyzing gadgets.
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