This paper presents an overview of the technologies for in-loop processing and filtering in the Versatile Video Coding (VVC) standard. These processes comprise luma mapping with chroma scaling, deblocking filter, sample adaptive offset, adaptive loop filter and cross-component adaptive loop filter. They are qualified as “in-loop” because they are applied inside the encoding and decoding loops, before storing the pictures in the decoded picture buffer. The filters are complementary and address different purposes. Luma mapping with chroma scaling aims at adaptively modifying the coded samples distribution for improved coding efficiency. The deblocking filter aims at reducing blocking discontinuities. Sample adaptive offset mostly aims at reducing artifacts resulting from the quantization of transform coefficients. Adaptive loop filter and cross-component adaptive loop filter are adaptive filters enabling to enhance the reconstructed signal, using for instance Wiener-filter encoding approaches. The paper provides an overview of the in-loop filtering process and a detailed description of the filtering algorithms. Objective compression efficiency results are provided for each filter, with indication of cumulative coding gains. Subjective benefits are illustrated. Implementation issues considered during the design of the VVC in-loop filters are also discussed.

Adaptive streaming technologies are used heavily to deliver video content over the Internet, but still face problems in networks with limited bandwidth, for videos with ultra-high definition (UHD) or greater, even when using the latest High-Efficiency Video Coding (HEVC) standard. In this paper, we propose a solution to these problems based on three things: a specific HEVC-compliant motion-constrained tiled encoding allowing tile-based decoding and rendering; a specific derivation of the ISO base media file format enabling a tile-based encapsulation and delivery; and their use in adaptive streaming of tiled video content using MPEG dynamic adaptive streaming over HTTP requiring only a single standard HEVC decoder on the client side. We present an evaluation of constrained and unconstrained HEVC tiling compression, and also of the associated packaging and streaming overheads. The results of this evaluation indicate that our approach incurs a complete overhead of at most 7.3% for UHD content, at 10 Mbps or above, when split in 25 tiles, and only 3.5% for nine tiles, when compared with the streaming of the non-tiled video. Our approach also comes with a small impact on client-side players but provides the interesting benefit of being able to deliver UHD videos over bandwidth-constrained networks adaptively, based on its tiled content.

Dynamic Adaptive Streaming over HTTP (DASH) is broadly deployed on the Internet for live and on-demand video streaming services. Recently, a new version of HTTP was proposed, named HTTP/2. One of the objectives of HTTP/2 is to improve the end-user perceived latency compared to HTTP/1.1. HTTP/2 introduces the possibility for the server to push resources to the client. This paper focuses on using the HTTP/2 protocol and the server push feature to reduce the start-up delay in a DASH streaming session. In addition, the paper proposes a new approach for video adaptation, which consists in estimating the bandwidth, using WebSocket (WS) over HTTP/2, and in making partial adaptation on the server side. Obtained results show that, using the server push feature and WebSocket layered over HTTP/2 allow faster loading time and faster convergence to the nominal state. Proposed solution is studied in the context of a direct client-server HTTP/2 connection. Intermediate caches are not considered in this study.

This paper presents a new approach for a scalable extension of High Efficiency Video Coding (HEVC) intra pictures. The proposed scalable intra codec targets very low complexity together with coding efficiency, and thus employs only one prediction mode, known as inter layer intra prediction. The main novelty of the codec is a new fast rate-distortion optimization approach, which is founded on the rate-distortion theory. It exploits precomputed offline information for the reduction of the computational complexity, and a new allocation process for the balancing of the coded information. The precomputed information is a set of optimal quantifiers and associated rate-distortion curves for predetermined statistical models that are used for efficiently coding the Discrete Cosine Transform channels. This low-complexity intra coding tool has been proposed to the scalable HEVC call for proposals. In the scalable standard testing conditions, the proposed intra codec shows compression performance with a bit-rate increase of around 10% only compared with the HEVC single-layer encoder.

After having issued the version 1 of the new video coding standard HEVC, ISO-MPEG and ITU-T VCEG groups are specifying its scalable extension. The candidate schemes are based on a multi-layer multi-loop coding framework, exploiting inter-layer texture and motion prediction and full base layer picture decoding. Several inter-layer prediction tools have been explored, implemented either using high-level syntax or block-level core HEVC design changes. One of these tools, Generalized Residual Prediction (GRP), has been extensively studied during several meeting cycles. It is based on second order residual prediction, exploiting motion compensation prediction residual in the base layer. This paper is focused on this new mode. The principle of GRP is described with an analysis of several implementation variants completed by a complexity analysis. Performance of these different implementations is provided, showing that noticeable gains can be obtained without significant complexity increase compared to a simple scalable design comprising only texture and motion inter-layer prediction.

We propose a new hierarchical approach to resolution scalable lossless and near-lossless (NLS) compression. It combines the adaptability of DPCM schemes with new hierarchical oriented predictors to provide resolution scalability with better compression performances than the usual hierarchical interpolation predictor or the wavelet transform. Because the proposed hierarchical oriented prediction (HOP) is not really efficient on smooth images, we also introduce new predictors, which are dynamically optimized using a least-square criterion. Lossless compression results, which are obtained on a large-scale medical image database, are more than 4% better on CTs and 9% better on MRIs than resolution scalable JPEG-2000 (J2K) and close to nonscalable CALIC. The HOP algorithm is also well suited for NLS compression, providing an interesting rate-distortion tradeoff compared with JPEG-LS and equivalent or a better PSNR than J2K for a high bit rate on noisy (native) medical images.

A new adaptive approach for lossless and near-lossless scalable compression of medical images is presented. It combines the adaptivity of DPCM schemes with hierarchical oriented prediction (HOP) in order to provide resolution scalability with better compression performances. We obtain lossless results which are about 4% better than resolution scalable JPEG2000 and close to non scalable CALIC on a large scale database. The HOP algorithm is also well suited for near-lossless compression, providing interesting rate-distortion trade-off compared to JPEG-LS and equivalent or better PSNR than JPEG2000 for high bit-rate on noisy (native) medical images.

This paper proposes to introduce Lanczos interpolation filters as wavelet atoms in an optimized decomposition for embedded lossy to lossless compression of biomedical images. The decomposition and the Lanczos parameter are jointly optimized in a generic packet structure in order to take into account the various contents of biomedical imaging modalities. Lossless experimental results are given on a large scale database. They show that in comparison with a well known basis using 5/3 biorthogonal wavelets and a dyadic decomposition, the proposed approach allows to improve the compression by more than 10% on less noisy images and up to 30% on 3D-MRI while providing similar results on noisy datasets.