Version 1 of VVC specification was released in July 2020. VVC is the successor of HEVC, with 40 to 50% better compression. It inherently supports additional features such as sub-pictures and multi-layer coding: different profiles are specified, including multilayer profiles for 4:2:0 and 4:4:4 content. These profiles give access to spatial scalability using a single decoder instance. This paper reports evaluation tests of the VVC 4:2:0 multilayer profile, in a spatially scalable configuration with two layers and a spatial resolution ratio of 2x both vertically and horizontally. The tests were performed using various bitrate allocations between the base layer and the enhancement layer, and the best tradeoff leading to optimal enhancement layer compression performance was investigated. Both objective and subjective evaluations have been carried out and report that Scalable-VVC can be on par or outperform single-layer coding in specific conditions. The paper also addresses coding complexity and shows that customizing the encoding parameters can lead to large encoding time savings. Coding performance comparisons between dual layer solutions, here scalable VVC and LCEVC, are also provided.
KEYWORDS: Video, Spatial resolution, Scalable video coding, Video coding, Video compression, Televisions, Mobile communications, Linear filtering, Image resolution, Data processing
High Definition Television is currently a hot topic that generates research and commercial interests in video industry. This paper relates to video coding devices and methods for easy and fast conversion of video formats for digital TV. The proposed algorithm provides a generic framework that allows any video format conversions in a scalable way. We focused particularly on solutions to extract Standard Definition video signals from High Definition ones. Practically, we address spatial scalability solutions between video formats that are not linked up by dyadic decomposition and/or istropic decomposition. The challenge is to generate a single scalable bitstream in a compliant way that permits any spatial resolution decodings while saving bandwidth compared to classical simulcast approaches. Practically, our scalable coder is based on a multi-layer approach and on frame scales and borders introduction. Moreover, to encode/decode the macroblocks of the high resolution pictures, usual video coding approaches exploit the inheritance knowing the macroblocks of the decoded low resolution pictures. Approaches for inter-layer prediction of motion data have already been proposed for dyadic decompositions but do not work in case of non dyadic inter-layer resolutions. Consequently we propose a different managing of the inter layer prediction. All these techniques have been successfully implemented in the MPEG-SVC reference software. Obtained results achieve good coding efficiency and are comparable to the simulcast state of the art while providing features induced by scalability.
In this paper, we present a mesh-based motion estimation scheme for image sequence. Nodal motion vectors optimization is performed by using a multi-resolution differential method. Because our final aim is mesh tracking throughout a video sequence with optimized reconstruction, neither backward tracking nor forward tracking is well suited. One motivation of our work is to take advantage of both forward tracking (which enables tracking) and backward tracking (for its efficiency) in a `backward in forward' method. For the optimization of the nodal motion vectors, we also propose a novel approach with multi-resolution and several hierarchy levels, which, in addition, makes it possible scalable representation. This is achieved with a progressive representation defined according to a rate distortion criterion. Results are presented to illustrate the proposed methods.
Recent development in video coding research deals with the use of hierarchical and/or adaptative mesh for video representation. Concurrently, transmitted bit rates have to be reduced to adapt to the network available bandwidth. Some previous works deal with adaptative node sampling according to image content. However, adaptative hierarchical proposed approaches do not optimize a compromise between distortion and bitrate: the representation coding cost is often stated but not taken into account as a constraint. Compared to these methods, this paper proposes for considering an adaptative hierarchical mesh based representation whose splitting criterion optimizes both the coding cost and the image rendering. Jointly, node value optimization, adaptative quantization, cheap coding tree and a wavelet approach are presented. To illustrate our different proposed methods, experimental results are shown and compared to the JPEG picture coding format.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.