- Open Access
Implementation of fast HEVC encoder based on SIMD and data-level parallelism
© Ahn et al.; licensee Springer. 2014
- Received: 18 June 2013
- Accepted: 5 March 2014
- Published: 26 March 2014
This paper presents several optimization algorithms for a High Efficiency Video Coding (HEVC) encoder based on single instruction multiple data (SIMD) operations and data-level parallelism. Based on the analysis of the computational complexity of HEVC encoder, we found that interpolation filter, cost function, and transform take around 68% of the total computation, on average. In this paper, several software optimization techniques, including frame-level interpolation filter and SIMD implementation for those computationally intensive parts, are presented for a fast HEVC encoder. In addition, we propose a slice-level parallelization and its load-balancing algorithm on multi-core platforms from the estimated computational load of each slice during the encoding process. The encoding speed of the proposed parallelized HEVC encoder is accelerated by approximately ten times compared to the HEVC reference model (HM) software, with minimal loss of coding efficiency.
- HEVC encoder
- SIMD implementation
- Slice-level parallelism
- Load balancing
Along with the development of multimedia and hardware technologies, the demand for high-resolution video services with better quality has been increasing. These days, the demand for ultrahigh definition (UHD) video services is emerging, and its resolution is higher than that of full high definition (FHD), by a factor of 4 or more. Based on the market demands, ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) have organized Joint Collaborative Team on Video Coding (JCT-VC) and standardized High Efficiency Video Coding (HEVC), whose target coding efficiency was twice better than that of H.264/AVC . In the near future, HEVC is expected to be employed for many video applications, such as video broadcasting and video communications.
Historically, MPEG-x and H.26x video compression standards employ the macro-block (MB) as one basic processing unit , and its size is 16 × 16. However, HEVC supports larger sizes of the basic processing unit, called coding tree unit (CTU), from 8 × 8 to 64 × 64. A CTU is split into multiple coding units (CU), in a quad-tree fashion . Along with the CU, the prediction unit (PU) and transform unit (TU) are defined, and their sizes and shapes are more diverse than the prior standard technologies [4, 5]. On top of them, many advanced coding tools that improve prediction, transform, and loop filtering are employed to double the compression performance compared with H.264/AVC. However, the computation requirement of HEVC is known to be significantly higher than that of H.264/AVC because HEVC has more prediction modes, larger block size, longer interpolation filter, and so forth.
Typically, a huge number of rate-distortion (RD) cost computations are required to find the best mode from 64 × 64 to 8 × 8 block sizes in the encoder side for HEVC. With respect to applications, HEVC would be employed for ultrahigh-resolution video services. For such cases, fast video coders are required to process more data with a given processing power. Thus, parallelization techniques would be crucial, with multiple low-power processors or platforms. The single instruction multiple data (SIMD) implementation of the most time-consuming modules on HM 6.2 encoders was proposed . This work implemented the cost functions, transformation, and interpolation filter with SIMD, and it reported that the average time saving obtained is approximately 50% to 80%, depending on the modules. Wavefront parallel processing (WPP) for HEVC encoders and decoders was introduced . For the decoder case, they achieved parallel speed-up by a factor of 3. The acceleration factor of the wavefront parallelism is in general saturated into 2 or 3 due to data communication overhead, epilog, and prolog parts. There are no works that incorporate all the parallel algorithms, with maximum harmonization for fast HEVC encoders. In this paper, we focus on load-balanced slice parallelization, with optimization implementation of HEVC. This paper presents several optimization techniques using SIMD operations for the RD cost computations and transforms for variable block sizes. In addition, motion estimation is also efficiently implemented with a frame-based processing to reduce the number of redundant filtering. For data-level parallelization, this paper demonstrates how to allocate encoding jobs to all the available cores through the use of complexity estimation. As a result, it is possible to achieve load-balanced slice parallelism in HEVC encoders to significantly reduce the average encoding time. With all the proposed techniques, the optimized HEVC encoder achieves a 90.1% average time saving within 3.0% Bjontegaard distortion (BD) rate increases compared to HM 9.0 reference software.
The paper is organized as follows. Section 2 presents a complexity analysis of HEVC encoder, and Section 3 introduces basic data-level parallelisms for video encoders. In Section 4, the SIMD optimization for cost functions and transform, as well as frame-level implementation of interpolation filter, is explained in detail. A slice-level parallelization technique with a load-balancing property is proposed in Section 5. Section 6 shows the performance and numerical analysis of the proposed techniques. Finally, Section 7 concludes the work, with further research topics.
In this section, the complexity of HEVC encoder is investigated, and critical modules can be identified based on the complexity analysis. In this work, HM 9.0 reference software  was used for HEVC encoder analysis. Note that it was used as the base software for our optimization. A HEVC encoder can be mainly modularized into five parts: entropy coding, intra prediction, inter prediction, transform quantization, and loop filter. The cycle analyzer, Intel® VTune™ Amplifier XE 2013  on Intel® Core™ i7-3960 K processor, was employed to measure the number of cycles for each module, in cases of the random access (RA) and low-delay (LD) test configurations, under the common test conditions . Note that class B (1,920 × 1,080) and class C (832 × 480) sequences were used.
Percentages of computational cycles of HM 9.0 encoder
In-loop filter (de-blocking filter)
In-loop filter (sample adaptive offset)
Percentages of computational cycles, depending on CU sizes and modes
Ratio in each CU size (%)
64 × 64
32 × 32
16 × 16
8 × 8
Percentages of computational cycles of top four functions
Data-level and function-level parallelization approaches are widely used for high-speed video codecs. In particular, function-level parallel processing is frequently used for hard-wired implementations. Note that function-level parallel processing is not easily implemented mainly due to difficulties of load balancing and longer development period. Data-level parallel processing is relatively easy to be employed for video encoders because the data processing flows are the same for all the data. The data-level parallelism for HEVC can be conducted in terms of CU-, slice-, and frame-level ones. In addition, HEVC contains a parallel tool, called tile, which divides a picture into multiple rectangles . In tile partitioning, the number of CTUs adjacent to boundaries of tile partitions is less than that of slices. From this fact, tile partitioning can yield slightly lower coding loss in compression efficiency compared to an implementation with the same number of slices .
For parallel implementations, we need to consider several factors, such as throughput and core scalability, as well as coding efficiency. Note that the core scalability means how much we need to change an implementation, depending on an increasing or decreasing number of cores. In addition, the throughput can be improved with parallel processing as compared with the single processing unit. However, many video coding algorithms, in general, have dependencies among neighboring coding units, neighboring frame, earlier-coded syntaxes, and so on. At the same time, we need to consider the coding efficiency degradation from the parallelization. Even though the throughput can be improved with parallel processing, it is not desirable that the coding efficiency is significantly degraded. Regarding the core scalability, it is better to employ a scalable parallelization method that can be easily extended for an increasing number of cores. If not, we are required to change the implementation, depending on the number of cores.
The 2D wavefront algorithm  has been used for the parallelization of video coding in CTU level. This coding tool does not impact the coding gain, but there is a limitation in the parallelization factor, even with many cores, due to coding dependence. Frame-level parallelization can be also used for non-reference bidirectional pictures; however, it depends on the encoding of reference structures.
As mentioned before, the slice-level parallelism has relatively high coding losses of around 2% to 4% compared to tile-level parallelism and wavefront processing . However, slice-level parallelism has an advantage that the slice partitioning is more flexible and accurate for picture partitioning, by adjusting the number of CTUs, compared to the tile partitioning. Note that the tiles within the same row and column should use the same tile width and height, respectively. Slice-level parallelism of a fine-grained load balancing can yield additional encoding speed-up compared to the tile levels. WPP has the advantage that the loss of parallelization is relatively small compared to other parallelization methods. However, the acceleration factor of WPP is not so high compared to slice- or tile-level parallelism because WPP has prolog and epilog so that parts of the cores are inactivated. It is not easy to utilize all the cores with WPP on average. In our work, slice-level parallelism was chosen for the acceleration of parallelization. In addition, slice partitioning is widely used for the packetizing of bitstreams for error resiliencies, in practical video encoders and services.
There are two main criteria to divide a picture into multiple slices. One is an equal bitrate, and the other is the same number of CTUs for all the slices. The first one cannot be easily employed for parallel encoding because we cannot define the target bit prior to actual encoding. For the second method, we can easily use the same number of CTUs at a time.
In this section, two software optimization methods, frame-level processing and SIMD implementation, for three most complex functions at the function-level are presented. The proposed software optimization methods have several advantages to accelerate HEVC encoders without any bitrate increase.
4.1 Frame-level interpolation filter in HEVC encoder
The HEVC DCT-based interpolation filter (DCT-IF), which is used for obtaining fractional sample positions, is the most complex function, especially with motion estimation in encoders. Instead of using 6-tap and bilinear interpolation filters of H.264/AVC, HEVC adopts 8(7)-tap DCT-IF for luminance components, and 4-tap DCT-IF for chrominance components . Furthermore, all of the fractional position samples are derived by increasing the number of filter taps without intermediate rounding operations which can reduce potential rounding errors compared to H.264/AVC. In order to determine the optimal CU size and coding modes, HM encoder uses a recursive scheme for the RD optimization process. In particular, the PU-level interpolation filter causes iterative memory accesses for the same positions redundantly. Excessive memory accesses significantly increase encoding time due to the limit of memory bandwidth. Actually, the DCT-IF occupies approximately 30% to 35% of the total cycles in the HM encoder. We adopt a frame-level interpolation filter to reduce redundant memory accesses. The frame-level interpolation filter avoids redundant execution that occurs in the RD optimization process and enables parallel process with independency among neighboring blocks. However, it requires the additional amount of memory for 15 factional samples per integer sample in an entire frame. In addition, SIMD instructions and multi-thread processes using OpenMP and GPU can be easily used for fast encoding.
4.2 SIMD implementation of cost function and transformation
To reduce the computational load of the RD optimization, early termination and mode competition algorithms have been adopted in HM reference software [23–25]. However, these fast encoding algorithms cause different encoding complexities among different slices. To maximize parallelism of the data-level task partition, an accurate load balance for slice parallelization is required. Several works [26, 27] have been conducted to achieve accurate load balance for slice parallelization. In Zhang's algorithm , the adaptive data partitioning for MPEG-2 video encoders was proposed by adjusting computational loads based on the complexity of a previously encoded frame of the same picture type. In Jung's algorithm , the adaptive slice partition algorithm was proposed to use early-decided coding mode for macro-blocks in H.264/AVC. In the conventional algorithm, a quantitative model was designed to estimate the computational load associated with each candidate MB mode group. However, in order to apply slice-level parallelism to a HEVC encoder, we need to focus on CTU structures, variable block sizes, and coding modes. In this section, a complexity estimation model and adaptive slice partition algorithm to achieve load-balanced slice parallelization are proposed.
5.1 Complexity estimation model
Normalized complexity for variable CU size and mode
64 × 64
32 × 32
16 × 16
8 × 8
Pearson product moment correlations of the actual and predicted times
Pearson product moment correlation
Class A (2,560 × 1,600)
Class B (1,920 × 1,080)
Class C (832 × 480)
5.2 Adaptive slice partitioning using characteristics of temporal layers
According to HEVC common test condition 
Profile: HEVC main profile (MP) 
Level: Level 4.1 
Encoding structure: RA and LD
QP value: 22, 27, 32, 37
Test sequences: HEVC common test sequences (classes B and C) in Table 6
Class B (1,920 × 1,080)
Class C (832 × 480)
where Etimeanchor is the encoding time of the anchor encoder and Etimeproposed is the proposed method.
Firstly, the ATS comparison between the anchor and the proposed software optimizations will be shown. Secondly, the coding efficiency of the slice parallelism using OpenMP will be presented for the four-slice case. Thirdly, the coding efficiency of the proposed load-balanced slice parallelism will be presented. Finally, the coding efficiency of the overall proposed encoder based on software optimization and parallelization will be evaluated, comparing to the HM 9.0 reference encoder.
HM 9.0 vs. optimized HEVC encoder software
Frame-level IF (B)
A + B
Frame-level IF (B)
A + B
HM 9.0 vs. slice parallelization using OpenMP
BD-BR, ATS, and ALS for slice and load-balanced slice parallelization
HM 9.0 vs. the proposed accelerated and parallelized HEVC encoder
BD-BR, BD-PSNR, and ATS of the proposed HEVC encoder for class A (2,560 × 1,600)
In this paper, the computational complexity of the HM 9.0 encoder was analyzed for acceleration and parallelization of the HEVC encoder. We identified five key modules for the HM 9.0 encoder, requiring dominant computing cycles. Based on the complexity analysis, two software optimization methods were used for acceleration: the frame-level interpolation filter and SIMD implementation. In addition, load-balanced slice parallelization is proposed. Software optimization methods achieve 33.56% of the average time saving, with any coding loss. In addition, load balancing for the slice parallelization method achieves about 10% of average time saving compared to uniform slice partition. The overall average time saving of the proposed HEVC encoder yields approximately 90% compared to HM 9.0 with acceptable coding loss. HEVC encoder with the proposed methods can compress full HD videos at approximately 1 fps speed in a commercial PC environment, without any hardware acceleration.
Further study will be focused on additional software optimization, fast encoding algorithm, and tile-level parallel processing for real-time encoder of HEVC.
This research was partly supported by the IT R&D program of MSIP/KEIT [10039199, A Study on Core Technologies of Perceptual Quality based Scalable 3D Video Codecs], the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-1011) supervised by the NIPA (National IT Industry Promotion Agency), and the grant from the Seoul R&BD Programs (SS110004M0229111).
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