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Comparative Rate-Distortion-Complexity Analysis of HEVC and AVC Video Codecs

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TLDR
The rate-distortion-complexity of High Efficiency Video Coding (HEVC) reference video codec (HM) and compares the results with AVC reference codec (JM) is analyzed and the bottlenecks of HM codec are revealed and implementation guidelines for future real-time HEVC codecs are provided.
Abstract
This paper analyzes the rate-distortion-complexity of High Efficiency Video Coding (HEVC) reference video codec (HM) and compares the results with AVC reference codec (JM). The examined software codecs are HM 6.0 using Main Profile (MP) and JM 18.0 using High Profile (HiP). These codes are benchmarked under the all-intra (AI), random access (RA), low-delay B (LB), and low-delay P (LP) coding configurations. In order to obtain a fair comparison, JM HiP anchor codec has been configured to conform to HM MP settings and coding configurations. The rate-distortion comparisons rely on objective quality assessments, i.e., bit rate differences for equal PSNR. The complexities of HM and JM have been profiled at the cycle level with Intel VTune on Intel Core 2 Duo processor. The coding efficiency of HEVC is drastically better than that of AVC. According to our experiments, the average bit rate decrements of HM MP over JM HiP are 23%, 35%, 40%, and 35% under the AI, RA, LB, and LP configurations, respectively. However, HM achieves its coding gain with a realistic overhead in complexity. Our profiling results show that the average software complexity ratios of HM MP and JM HiP encoders are 3.2× in the AI case, 1.2× in the RA case, 1.5× in the LB case, and 1.3× in the LP case. The respective ratios with HM MP and JM HiP decoders are 2.0×, 1.6×, 1.5×, and 1.4×. This paper also reveals the bottlenecks of HM codec and provides implementation guidelines for future real-time HEVC codecs.

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Citations
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Journal ArticleDOI

Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors

TL;DR: This paper analyzes the ME structure in HEVC and proposes a parallel framework to decouple ME for different partitions on many-core processors and achieves more than 30 and 40 times speedup for 1920 × 1080 and 2560 × 1600 video sequences, respectively.
Journal ArticleDOI

A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors

TL;DR: This paper proposes a parallel framework to decide coding unit trees through in-depth understanding of the dependency among different coding units, and achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.
Proceedings ArticleDOI

Learned Video Compression

TL;DR: This work presents a new algorithm for video coding, learned end-to-end for the low-latency mode, which outperforms all existing video codecs across nearly the entire bitrate range, and is the first ML-based method to do so.
Journal ArticleDOI

Fast HEVC Encoding Decisions Using Data Mining

TL;DR: Extensive experiments and comparisons demonstrate that the proposed early termination schemes achieve the best rate-distortion-complexity tradeoffs among all the compared works.
Journal ArticleDOI

Efficient Mode Decision Schemes for HEVC Inter Prediction

TL;DR: Three key optimization techniques can be seamlessly incorporated in the existing control structures of the HEVC reference encoder without limiting its potential parallelization, hardware acceleration, or speed-up with other existing encoder optimizations.
References
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Journal ArticleDOI

Highly efficient predictive zonal algorithms for fast block-matching motion estimation

TL;DR: Two techniques are proposed, the generalized motion vector predictor and the adaptive threshold calculation, that can be used to significantly improve the performance of many existing fast ME algorithms and create two new algorithms, named advanced predictive diamond zonal search and predictive MV field adaptive search technique.
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