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.

The increasing penetration of acquisition and display devices for Light Field (LF) content in the consumer market leads to the high proliferation of this new immersive media. This growing interest to LF images thus urgently raises the question of their compression. In this paper, we propose a convolutional neural networks (CNN)-based LF image coding scheme including both Rate Distortion Optimization (RDO) and post-processing steps. First, at the encoder side, the views are rearranged in sparse and dropped set of views. The former are compressed with a standard encoder and transmitted, while the dropped views are either linearly approximated or synthesized by a CNN using the encoded views as input. This choice is made on the basis of the proposed RDO process. At the decoder side, once the dropped views are either linearly approximated or synthesized by a CNN block, a post-processing step is performed to further enhance the quality of the reconstructed views. This post-processing block is based on superpixel to pixel-matching. Experimental results show that the proposed scheme provides views with high visual quality and overcomes the state-of-the-art LF image compression solutions by -30% in terms of BD-BR and 0.62 dB in BD-PSNR.

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.

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.

The scalable video coding enables to compress video contents into a hierarchical layered representation, each layer depicts an enhanced version of the underlying layer. SHVC is the scalable extension of HEVC and enables spatial, SNR, color-gamut, codec and bitdepth scalability. It has been proved, in the MPEG investigations prior to the recent Call for Evidence, that SHVC can support SDR-to-HDR scalability by using the color gamut scalability, when SDR and HDR signals are placed in different color gamuts. This way, SHVC can be used to address future backward compatible issues in the HDR and WCG services deployment. In this paper, we exploit the impact of bitrate ratio over performance in scalable schemes to design an adaptive rate control algorithm suitable for such deployment, considering adjustable quality and bandwidth constraints. Our method dynamically adjusts the bitrate ratio between two layers during encoding in the most quality-related optimal way under specified constraints. The proposed method is tested on scalable combinations of HD/UHD, R.709/DCI-P3/R.2020 and SDR/HDR video contents, and reduces the average overhead introduced by SHVC compared to the single-layer HEVC encoding by 23%.

Summary form only given. In this paper we present efficient parallelization implementations for different stages of the HEVC decoder, which are LCU decoding, deblocking filtering and SAO filtering. Each of the stages are parallelized in separate passes. The LCU decoding is parallelized using Wave front Parallel Processing (WPP). Deblocking and SAO filtering are parallelized by segmenting each picture into separate regions of consecutive LCU rows and processing each of the regions in a concurrent fashion. On a 6 core machine with 6 threads running concurrently, experimental results showed an average accelerating factor of 4.6, 5, 5.35 for the LCU decoding stage and 4.5, 4.9, 5 for deblocking filtering stage and 4, 4.5 and 5 for SAO filtering stages on HD, 1600p and 2160p sequences respectively.

We present a method for segmenting multilevels images of documents. The documents are considered difficult ones in the sense they may contain text paragraphs with different orientations and shapes, mixed with graphics and photographs. The proposed method extracts and separates blocks of text lines (printed or handwritten characters) and headers as well as stroke structures. The generic approach is first based on a multiscale analysis with the use of a pyramid representation of the image. At each level, text location is performed by a line borders detection scheme. Then, an efficient bottom-up procedure generates bodies (text paragraphs) as the output of algebric transformations upon a set of four directed graphs associated with the topological relationships of physical components.