In this paper, we present a technique that is known as Residual Sign Prediction in Transform Domain (TDRSP) and is aimed at increasing compression performance by reducing the bits overhead of residue sign. These signs are typically coded by entropy coders in bypass mode that results in the high cost of sign bins which require 1 bit per bin in a bitstream. TDRSP allows us to reduce this cost by predicting residue signs so that the probability of a guess is significantly higher than 50%. Hence, arithmetic coding with contexts becomes applicable to the signs. In contrast to Residual Sign Prediction (RSP) performed in spatial domain, TDRSP avoids switching between domains and carries out calculations completely in transform domain to efficiently decrease the computational complexity of RSP. Simulations performed on top of Versatile Video Coding test model reference software (VTM-1.0) in accordance with the Joint Video Experts Team common test conditions show that more than 2.0% and up to 1.8% of the Bjøntegaard Delta rate can be achieved for All Intra and Random Access configurations, respectively.

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.

The standardization process for Versatile Video Coding (VVC), the next generation video coding standard, was launched in 2018, after several recent advances in video coding technologies had been investigated under the Joint Video Experts Team (JVET) of ITU-T VCEG and ISO/IEC MPEG experts. The recent standard development status (up to VVC working draft 2) shows that the VTM software, the test model for this VVC standard, can achieve over 23% average coding gain under random access configuration when compared to the HM software, the test model of HEVC standard. This paper gives a review of recently developed video coding technologies that have been either adopted into the VVC working draft as part of the standard or under further evaluation for potential inclusions.

Intra coding is an essential part of all video coding algorithms and applications. Additionally, intra coding algorithms are predestined for an efficient still image coding. To overcome limitations in existing intra coding algorithms (such as linear directional extrapolation, only one direction per block, small reference area), we propose non-linear Contour-based Multidirectional Intra Coding. This coding mode is based on four different non-linear contour models, on the connection of intersecting contours and on a boundary recall-based contour model selection algorithm. The different contour models address robustness against outliers for the detected contours and evasive curvature changes. Additionally, the information for the prediction is derived from already reconstructed pixels in neighboring blocks. The achieved coding efficiency is superior to those of related works from the literature. Compared with the closest related work, BD rate gains of 2.16% are achieved on average.

Machine learning (ML) has been producing major advances in several technological fields and can have a significant impact on media coding. However, fast progress can only happen if the ML techniques are adapted to match the true needs of compression. In this paper, we analyze why some straightforward applications of ML tools to compression do not really address its fundamental problems, which explains why they have been yielding disappointing results. From an analysis of why compression can be quite different from other ML applications, we present some new problems that are technically challenging, but that can produce more significant advances. Throughout the paper, we present examples of successful applications to video coding, discuss practical difficulties that are specific to media compression, and describe related open research problems.