Complexity Analysis Of Next-Generation VVC Encoding and Decoding

While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases. This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding. These extensive analyses are performed for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding). Results indicate that the VVC encoder and decoder are 5x and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI, respectively. Detailed analysis of coding tools reveals that in LD on average, motion estimation tools with 53%, transformation and quantization with 22%, and entropy coding with 7% dominate the encoding complexity. In decoding, loop filters with 30%, motion compensation with 20%, and entropy decoding with 16%, are the most complex modules. Moreover, the required memory bandwidth for VVC encoding/decoding are measured through memory profiling, which are 30x and 3x of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder.Comment: IEEE ICIP 202

Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatialtemporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.Comment: 10 pages, 10 figure

Overview of the Low Complexity Enhancement Video Coding (LCEVC) Standard

The Low Complexity Enhancement Video Coding (LCEVC) specification is a recent standard approved by the ISO/IEC JTC 1/SC 29/WG04 (MPEG) Video Coding. The main goal of LCEVC is to provide a standalone toolset for the enhancement of any other existing codec. It works on top of other coding schemes, resulting in a multi-layer video coding technology, but unlike existing scalable video codecs, adds enhancement layers completely independent from the base video. The LCEVC technology takes as input the decoded video at lower resolution and adds up to two enhancement sub-layers of residuals encoded with specialized low-complexity coding tools, such as simple temporal prediction, frequency transform, quantization, and entropy encoding. This paper provides an overview of the main features of the LCEVC standard: high compression efficiency, low complexity, minimized requirements of memory and processing power

A comprehensive video codec comparison

In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency