High efficiency wireless video networks for next generation of ENG services

This work has been partially funded by the CDTI under the FREEDOM (Servicios de Videocomunicaciones de Alta Eficiencia para Redes Inalámbricas de Nueva Generación en Movilidad) project, with Reference Number IDI20120486.Ruiz, D.; Giménez Gandia, JJ.; Gómez Barquero, D.; Anaya, JJ.; Fernández, FJ.; Valdés Francisco J.; Barjau, C.... (2013). High efficiency wireless video networks for next generation of ENG services. Waves. 5-16. http://hdl.handle.net/10251/55008S51

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks

A Research on Enhancing Reconstructed Frames in Video Codecs

A series of video codecs, combining encoder and decoder, have been developed to improve the human experience of video-on-demand: higher quality videos at lower bitrates. Despite being at the leading of the compression race, the High Efficiency Video Coding (HEVC or H.265), the latest Versatile Video Coding (VVC) standard, and compressive sensing (CS) are still suffering from lossy compression. Lossy compression algorithms approximate input signals by smaller file size but degrade reconstructed data, leaving space for further improvement. This work aims to develop hybrid codecs taking advantage of both state-of-the-art video coding technologies and deep learning techniques: traditional non-learning components will either be replaced or combined with various deep learning models. Note that related studies have not made the most of coding information, this work studies and utilizes more potential resources in both encoder and decoder for further improving different codecs.In the encoder, motion compensated prediction (MCP) is one of the key components that bring high compression ratios to video codecs. For enhancing the MCP performance, modern video codecs offer interpolation filters for fractional motions. However, these handcrafted fractional interpolation filters are designed on ideal signals, which limit the codecs in dealing with real-world video data. This proposal introduces a deep learning approach for all Luma and Chroma fractional pixels, aiming for more accurate motion compensation and coding efficiency.One extraordinary feature of CS compared to other codecs is that CS can recover multiple images at the decoder by applying various algorithms on the one and only coded data. Note that the related works have not made use of this property, this work enables a deep learning-based compressive sensing image enhancement framework using multiple reconstructed signals. Learning to enhance from multiple reconstructed images delivers a valuable mechanism for training deep neural networks while requiring no additional transmitted data.In the encoder and decoder of modern video coding standards, in-loop filters (ILF) dedicate the most important role in producing the final reconstructed image quality and compression rate. This work introduces a deep learning approach for improving the handcrafted ILF for modern video coding standards. We first utilize various coding resources and present novel deep learning-based ILF. Related works perform the rate-distortion-based ILF mode selection at the coding-tree-unit (CTU) level to further enhance the deep learning-based ILF, and the corresponding bits are encoded and transmitted to the decoder. In this work, we move towards a deeper approach: a reinforcement-learning based autonomous ILF mode selection scheme is presented, enabling the ability to adapt to different coding unit (CU) levels. Using this approach, we require no additional bits while ensuring the best image quality at local levels beyond the CTU level.While this research mainly targets improving the recent video coding standard VVC and the sparse-based CS, it is also flexibly designed to adapt the previous and future video coding standards with minor modifications.博士（工学）法政大学 (Hosei University

Improved sample adaptive offset for HEVC

High-Efficiency Video Coding (HEVC) is the newest video coding standard which can significantly reduce the bit rate by 50% compared with existing standards. One new efficient tool is sample adaptive offset (SAO), which classifies reconstructed samples into different categories, and reduces the distortion by adding an offset to samples of each category. Two SAO types are adopted in HEVC: edge offset (EO) and band offset (BO). Four 1-D directional edge patterns are used in edge offset type, and only one is selected for each CTB. However, single directional pattern cannot remove artifacts effectively for the CTBs, which contain edges in different directions. Therefore, we analyze the performance of each edge pattern applied on this kind of CTB, and propose to take advantage of existing edge classes and combine some of the them as a new edge offset class, which can adapt to multiple edge directions. All the combinations are tested, and the results show that for Low Delay P condition, they can achieve 0.2% to 0.5% bit rate reduction. © 2013 APSIPA