This paper summarises the design of the Cool-Chic candidate for the Challenge on Learned Image Compression. This candidate attempts to demonstrate that neural coding methods can lead to low complexity and lightweight image decoders while still offering competitive performance. The approach is based on the already published overfitted lightweight neural networks Cool-Chic, further adapted to the human subjective viewing targeted in this challenge.

We propose a lightweight learned video codec with 900 multiplications per decoded pixel and 800 parameters overall. To the best of our knowledge, this is one of the neural video codecs with the lowest decoding complexity. It is built upon the overfitted image codec Cool-chic and supplements it with an inter coding module to leverage the video’s temporal redundancies. The proposed model is able to compress videos using both low-delay and random access configurations and achieves rate-distortion close to AVC while outperforming other overfitted codecs such as FFNeRV. The system is made open-source: orange-opensource.github.io/Cool-Chic.

This paper summarises the design of the candidate ED for the Challenge on Learned Image Compression 2024. This candidate aims at providing an anchor based on conventional coding technologies to the learning-based approaches mostly targeted in the challenge.The proposed candidate is based on the Enhanced Compression Model (ECM) developed at JVET, the Joint Video Experts Team of ITU-T VCEG and ISO/IEC MPEG.Here, ECM is adapted to the challenge objective: to maximise the perceived quality, the encoding is performed according to a perceptual metric, also the sequence selection is performed in a perceptual manner to fit the target bit per pixel objectives.The primary objective of this candidate is to assess the recent developments in video coding standardisation and in parallel to evaluate the progress made by learning-based techniques. To this end, this paper explains how to generate coded images fulfilling the challenge requirements, in a reproducible way, targeting the maximum performance.

The next generation MPEG video coding standard is under development by the Joint Video Coding Experts Team (JVET). This new standard, called Versatile Video Coding (VVC), is expected by the end of 2020 and will offer better coding efficiency than its predecessor High Efficiency Video Coding (HEVC) standard. This coding gain is enabled by new coding tools such as more flexible block partitioning, more accurate Intra/Inter predictions, multiple transforms and adaptive in-loop filtering. In this paper we focus on the coding of the Intra Prediction Modes (IPM) that have been increased from 35 modes in HEVC to 67 modes in VVC. We propose a solution based on genetic algorithms to build an ordered list for the coding of IPM in the Joint Exploration Model (JEM) codec. We first give the theoretical upper bound performance in terms of required bits per IPM to encode the IPM using the available contextual information. The new ordering of the labels associated with more efficient codes is then proposed to efficiently leverage contextual informations available in the encoder and construct the Most Probable Modes (MPM) list. The proposed coding scheme enables to increase the BD-BR performance in average by 0.09% for the same level of complexity compared to the JEM.

In October 2015, MPEG and ITU jointly launched the Joint Video Exploration Team (JVET) to prepare the next generation of video coding standard beyond HEVC. The tools added over HEVC, grouped into the Joint Exploration Model 3.0 (JEM-3.0), achieve over 25% of coding gains compared to HEVC for the Random-Access mode. In inter-coding mode, the transform stage consists in the Adaptive Multiple Transforms (AMT) which puts classical DCT-2 in competition with alternative transforms. In this paper, it is proposed to improve the AMT stage in inter-coding. The proposed method enables the encoder either to turn-off the AMT or to adjust its number of transforms. The introduced transforms are split into 3 nested sets: n°1 includes DST-7 and DCT-8; n°2 contains n°1 plus DST-7/DCT-8 combinations; n°3 contains n°2 plus 4 transforms learnt according to a Rate-Distortion Optimized Transform criterion (RDOT) [1]. The encoder performs a multiple-passes Coding-Tree-Unit (CTU) encoding to select the best set and signals its index into the bitstream, in a differential way. Acceleration strategies are implemented to mitigate the complexity, such as pass-cancellation and fast CTU partitioning from one pass to another.

The High Efficiency Video Coding (HEVC) standard defines 35 Intra Prediction Modes (IPM) to provide an efficient compression of intra coded blocks. Those IPMs are signalled to the decoder through the use of three compression tools: prediction, clustering and coding. In this paper we provide improvements to these three tools through: new labels for the prediction, new tests for the clustering and new coding schemes for the coding. The most significant improvement consists in the provision of a cluster-dependent code: adapting the coding scheme to the available information enables the average symbol cost to get within close margin of the entropy of the data. The system providing the best compression efficiency based on these improvements is then computed, enabling significant reduction in the average cost required to code the IPMs. The proposed method builds a new coding system with the same complexity as HEVC with 0.41% bit-rates savings in All Intra coding configuration.