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

Abstract

Versatile Video Coding (VVC/H.266) is the next-generation international video coding standard and a successor to the widespread High Efficiency Video Coding (HEVC/H.265). This paper analyzes the rate-distortion-complexity characteristics of the VVC reference software (VTM10.0) by using HEVC reference software (HM16.22) as an anchor. In this independent study, the rate-distortion performance of VTM was benchmarked against HM with the objective PSNR, SSIM, and VMAF quality metrics and the associated encoder and decoder complexities were profiled at function level using Intel VTune Profiler on Intel Xeon E5-2699 v4 22-core processors. For a fair comparison, all our experiments were conducted under the VTM common test conditions (CTC) that define 10-bit configurations of the VTM codec for the addressed All Intra (AI), Random Access (RA), and Low Delay B (LB) conditions. The VTM CTC test set was also extended with complementary 4K UHD sequences to elaborate RD characteristics with higher resolutions. According to our evaluations, VTM improves the average coding efficiency over HM, depending on quality metric, by 23.0-23.9% under the AI condition, 33.1-36.6% under the RA condition, and 26.7-29.5% under the LB condition. However, the coding gain of VTM comes with 34.0×, 8.8×, and 7.5× encoding complexity over that of HM under the AI, RA, and LB conditions, respectively. The corresponding overhead of the VTM decoder stays steady at 1.8× across all conditions. This study also pinpoints the most complex parts of the VTM codec and discusses practical implementation aspects of prospective real-time VVC encoders and decoders.INDEX TERMS Common test conditions (CTC), HEVC test model (HM), High Efficiency Video Coding (HEVC), objective quality analysis, performance profiling, rate-distortion-complexity (RDC), UVG dataset, Versatile Video Coding (VVC), video codec, VVC test model (VTM). I. INTRODUCTIONUR society is surrounded by a myriad of media applications where digital video is of the essence. According to Cisco, the global IP video traffic will increase fourfold from 2017 and account for 82% of all IP traffic by 2022 [1]. Moreover, Comcast estimates that the prevailing COVID-19 crisis has increased Voice over Internet Protocol (VoIP) and videoconferencing by 210-285% and other video consumption by 20-40% over that of the prepandemic period [2]. This snowballing growth is mainly driven by the omnipresent connectivity and proliferation of advanced multimedia solutions that support emerging bandwidth-greedy formats like 4K/8K Ultra High Definition (UHD) or 360-degree omnidirectional videos.

VVC Search Space Analysis Including an Open, Optimized Implementation

“…The analyses provide insightful information, each with a different focus. In [25][26] [28] the runtime of different encoding aspects was measured and analyzed for different encoding quality settings. In [27][29] the encoding time spent per video channel and block size in intra-only coding was emphasized.…”

Beyond Bjøntegaard: Limits of Video Compression Performance Comparisons

“…As a consequence, new quality metrics such as the structural similarity index (SSIM) [7] or the video multi-method assessment fusion (VMAF) [8] were developed, which aim to have a higher correlation with the subjective quality of videos. Consequently, researchers started evaluating the performance of new compression algorithms replacing the classic PSNR in rate-distortion curves with these new quality metrics [9,10]. Furthermore, results from subjective testing were also employed replacing the PSNR with mean-opinion scores (MOS) [2,11].…”

Design Space Exploration of Practical VVC Encoding for Emerging Media Applications

“…It was introduced as successor to the widespread High Efficiency Video Coding (HEVC) [3]. VVC is able to improve coding efficiency for the same objective visual quality by more than 30% over HEVC [4] and the gap widens to 40% with subjective quality [5]. However, this quality increase comes at a cost of over eight times encoding complexity under the random access (RA) condition [4] of the common test conditions (CTC) [6].…”

“…Table I compares the most prominent VVC complexity analyses [4], [8], [13]- [21] with our work. The comparison includes the profiled encoder(s), the conditions, as well as the characterization of complexity, rate distortion (RD), and QT+MTT analyses.…”

“…The other listed works included more in-depth analyses [4], [8], [16]- [19]. The main contribution of Cerveira et al [16] was memory profiling results with the shares of memory accesses per each encoding stage.…”