Quantization and Entropy Coding in the Versatile Video Coding (VVC) Standard

Abstract

The paper provides an overview of the quantization and entropy coding methods in the Versatile Video Coding (VVC) standard. Special focus is laid on techniques that improve coding efficiency relative to the methods included in the High Efficiency Video Coding (HEVC) standard: The inclusion of trellis-coded quantization, the advanced context modeling for entropy coding of transform coefficient levels, the arithmetic coding engine with multi-hypothesis probability estimation, and the joint coding of chroma residuals. Beside a description of the design concepts, the paper also discusses motivations and implementation aspects. The effectiveness of the quantization and entropy coding methods specified in VVC is validated by experimental results. Index Terms-Versatile Video Coding (VVC), quantization, entropy coding, transform coefficient coding, video coding. I. INTRODUCTION T HE Versatile Video Coding (VVC) standard [1]-[3] is the most recent joint video coding standard of the ITU-T and ISO/IEC standardization organizations. It was developed by the Joint Video Experts Team (JVET), a partnership between the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). VVC was technically finalized in July 2020. It is published as ITU-T Rec. H.266 and ISO/IEC 23090-3 (MPEG-I Part 3).The primary objective of the new VVC standard is to provide a significant increase in compression capability compared to its predecessor, the High Efficiency Video Coding (HEVC) standard [4]. At the same time, VVC includes design features that make it suitable for a broad range of video applications. In addition to conventional video applications, it particularly addresses the coding of video with high dynamic range and wide color gamut, computer-generated video (e. g., for remote Manuscript

High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation

“…Computation throughput limitation incurred by the sequential data dependency in entropy context modeling was extensively investigated since the standardization of HEVC a decade ago. High-throughput and high-performance were then jointly evaluated during the development of entropy coding engine [22], [40]. Well-known examples include symbol parsing dependency unknitting, bins grouping, etc that more or less rely on the utilization of contextual correlation in local neighborhood.…”

“…Referring to (4), the DCT is often used with quantization Q(•) to generate quantized transform coefficients ĉm of predictive residues r m = (I − I p m ), yielding noise augmented residues rm in decoder for reconstruction. Here we use E(•) as the generic entropy coding engine for the coding of coefficients ĉm and side information m. In VVC [40], [58], notable CABAC is often used. Note that HiPT enforces the use of casual neighbors from available upper and/or left blocks of current I in Fig.…”

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“…Low-frequency non-separable transform (LFNST) [17,18] is newly adopted in VVC and is applied to the top-left low-frequency region (ROI) of the primary transformed coefficients. When the LFNST is applied, all primary transform coefficients excluding ROI are zeroed out [19,20], and the output of LFNST is further quantized and entropy-coded [21]. In this paper, we analyze the number of multiplications of the existing fast transform methods in the VVC standard, and we propose a new fast inverse transform using the number of non-zero coefficients based on linearity to reduce the number of multiplications.…”