Video Traffic Characteristics of Modern Encoding Standards: H.264/AVC with SVC and MVC Extensions and H.265/HEVC

abstract: Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC.View the article as published at https://www.hindawi.com/journals/tswj/2014/189481

Video Traffic Characteristics of Modern Encoding Standards: H.264/AVC with SVC and MVC Extensions and H.265/HEVC

Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC

Contributions to the solution of the rate-distorsion optimization problem in video coding

In the last two decades, we have witnessed significant changes concerning the demand of video codecs. The diversity of services has significantly increased, high definition (HD) and beyond-HD resolutions have become a reality, the video traffic coming from mobile devices and tablets is increasing, the video-on-demand services are now playing a prominent role, and so on. All of these advances have converged to demand more powerful standard video codecs, the more recent ones being the H.264/Advanced Video Coding (H.264/AVC) and the latest High Efficiency Video Coding (HEVC), both generated by the Joint Collaborative Team on Video Coding (JCT-VC), a partnership of the ITU-T Video Coding Expert Group (VCEG) and the ISO/IED Moving Picture Expert Group (MEPG). These two standards (and many others starting with the ITU-T H.261) rely on a hybrid model known as Differential Pulse Code Modulation (DPCM)/Discrete Cosine Transform (DCT) hybrid video coder, which involves a motion estimation and compensation phase followed by a transformation and quantization stages and an entropy coder. Moreover, each of these main subsystems is made of a number of interdependent and parametric modules that can be adapted to the particular video content. The main problem arising from this approach is how to choose as best as possible the combination of the different parametrizations to achieve the most efficient coding of the current content. To solve this problem, one of the solutions proposed (and the one adopted in both the H.264/AVC and the HEVC reference encoder implementations) is the process referred to as rate-distortion optimization, which chooses a parametrization of the encoder based on the minimization of a cost function that considers the trade-off between rate and distortion, weighted by a Lagrange multiplier (��) which has been empirically obtained for both the H.264/AVC and the HEVC reference encoder implementations, aiming to provide a robust solution for a variety of video contents. In this PhD. thesis, an exhaustive study of the influence of this Lagrangian parameter on different video sequences reveals that there are some common features that appear frequently in video sequences for which the adopted �� model (the reference model) becomes ineffective. Furthermore, we have found a notable margin of improvement in the coding efficiency of both coders when using a more adequate model for the Lagrangian parameter. Thus, contributions of this thesis are the following: (i) to prove that the reference Lagrangian model becomes ineffective in certain common situations; and (ii), propose generalized solutions to improve the robustness of the reference model, both for the H.264/AVC and the HEVC standards, obtaining important improvements in the coding efficiency. In both proposals, changes in the nature over the video sequence are taken into account, proposing models that adaptively consider the video content and minimize the increment in computational complexity.En las últimas dos décadas hemos sido testigos de importantes cambios en la demanda de codificadores de vídeo debido a múltiples factores: la diversidad de servicios se ha visto incrementada significativamente, la resolución high definition (HD) (e incluso mayores) se ha hecho realidad, el tráfico de vídeo procedente de dispositivos móviles y tabletas está aumentando y los servicios de vídeo bajo demanda son cada vez más comunes, entre otros muchos ejemplos. Todos estos avances convergen en la demanda de estándares de codificación de vídeo más potentes, siendo los más importantes el H.264/Advanced Video Coding (AVC) y el más reciente High Efficiency Video Coding (HEVC), ambos definidos por el Joint Collaborative Team on Video Coding (JCT-VC), una colaboraci´on entre el ITU-T Video Coding Expert Group (VCEG) y el ISO/IED Moving Picture Expert Group (MPEG). Estos dos estándares (y otros muchos, empezando con el ITU-T H.261) se basan en un modelo híbrido de codificador conocido como Differential Pulse Code Modulation (DPCM)/Discrete Cosine Transform (DCT), que está formado por una estimación y compensación de movimiento seguida de una etapa de transformación y cuantificación y un codificador entrópico. Además, cada uno de estos subsistemas está formado por un cierto número de módulos interdependientes y paramétricos que pueden adaptarse al contenido específico de cada secuencia de vídeo. El principal problema que surge de esta aproximación es cómo elegir de la forma más adecuada la combinación de las distintas parametrizaciones con el objetivo de alcanzar la codificación más eficiente posible del contenido que se está procesando. Para resolver este problema, una de las soluciones propuestas es el proceso conocido como optimización tasa-distorsión, que se encarga de elegir una parametrización para el codificador basada en la minimización de una función de coste que considera el compromiso existente entre la tasa y la distorsión, ponderado por un multiplicador de Lagrange (�) que ha sido obtenido de forma empírica para las implementaciones de referencia del codificador tanto del estándar H.264/AVC como del estándar HEVC, con el objetivo de proponer una solución robusta para distintos tipos de contenidos de vídeo. En esta tesis doctoral, un estudio exhaustivo de la influencia de este parámetro lagrangiano en distintas secuencias de vídeo revela que existen algunas características comunes que aparecen frecuentemente en secuencias de vídeo para las que el modelo � adoptado en las implementaciones de referencia resulta poco efectivo. Además, hemos encontrado un notable margen de mejora en la eficiencia de codificación de ambos codificadores usando un modelo más adecuado para este parámetro lagrangiano. Por consiguiente, las contribuciones de esta tesis son las que siguen: (i) probar que el modelo lagrangiano de referencia resulta inefectivo bajo ciertas situaciones comunes; y (ii), proponer soluciones generalizadas para mejorar la robustez del modelo de referencia, tanto en el caso de H.264/AVC como en el de HEVC, obteniendo mejoras importantes en eficiencia de codificación. En ambas propuestas se tienen en cuenta los cambios en la naturaleza del contenido de una secuencia de vídeo proponiendo modelos que se adaptan dinámicamente a dicho contenido variable y que tienen en cuenta el incremento en la complejidad computacional del codificador.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: José Prades Nebot.- Secretario: Carmen Peláez Moreno.- Vocal: Julián Cabrera Quesad

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

Towards visualization and searching :a dual-purpose video coding approach

In modern video applications, the role of the decoded video is much more than filling a screen for visualization. To offer powerful video-enabled applications, it is increasingly critical not only to visualize the decoded video but also to provide efficient searching capabilities for similar content. Video surveillance and personal communication applications are critical examples of these dual visualization and searching requirements. However, current video coding solutions are strongly biased towards the visualization needs. In this context, the goal of this work is to propose a dual-purpose video coding solution targeting both visualization and searching needs by adopting a hybrid coding framework where the usual pixel-based coding approach is combined with a novel feature-based coding approach. In this novel dual-purpose video coding solution, some frames are coded using a set of keypoint matches, which not only allow decoding for visualization, but also provide the decoder valuable feature-related information, extracted at the encoder from the original frames, instrumental for efficient searching. The proposed solution is based on a flexible joint Lagrangian optimization framework where pixel-based and feature-based processing are combined to find the most appropriate trade-off between the visualization and searching performances. Extensive experimental results for the assessment of the proposed dual-purpose video coding solution under meaningful test conditions are presented. The results show the flexibility of the proposed coding solution to achieve different optimization trade-offs, notably competitive performance regarding the state-of-the-art HEVC standard both in terms of visualization and searching performance.Em modernas aplicações de vídeo, o papel do vídeo decodificado é muito mais que simplesmente preencher uma tela para visualização. Para oferecer aplicações mais poderosas por meio de sinais de vídeo,é cada vez mais crítico não apenas considerar a qualidade do conteúdo objetivando sua visualização, mas também possibilitar meios de realizar busca por conteúdos semelhantes. Requisitos de visualização e de busca são considerados, por exemplo, em modernas aplicações de vídeo vigilância e comunicações pessoais. No entanto, as atuais soluções de codificação de vídeo são fortemente voltadas aos requisitos de visualização. Nesse contexto, o objetivo deste trabalho é propor uma solução de codificação de vídeo de propósito duplo, objetivando tanto requisitos de visualização quanto de busca. Para isso, é proposto um arcabouço de codificação em que a abordagem usual de codificação de pixels é combinada com uma nova abordagem de codificação baseada em features visuais. Nessa solução, alguns quadros são codificados usando um conjunto de pares de keypoints casados, possibilitando não apenas visualização, mas também provendo ao decodificador valiosas informações de features visuais, extraídas no codificador a partir do conteúdo original, que são instrumentais em aplicações de busca. A solução proposta emprega um esquema flexível de otimização Lagrangiana onde o processamento baseado em pixel é combinado com o processamento baseado em features visuais objetivando encontrar um compromisso adequado entre os desempenhos de visualização e de busca. Os resultados experimentais mostram a flexibilidade da solução proposta em alcançar diferentes compromissos de otimização, nomeadamente desempenho competitivo em relação ao padrão HEVC tanto em termos de visualização quanto de busca

Super-Resolution and Scalable Video Coding

Bits on the wire not only impact video quality delivered to customers but also drive the costs of video streaming services. This project aims at building state-of-the-art deep-learning-based video super-resolution (VSR) algorithms while addressing the compression artifact, and then integrate the VSR into the Scalable Extension of High Efficiency Video Coding (SHVC) by replacing the inter-layer upscaler with the VSR upscaler, and benchmark the codec performance. The discrete cosine transform upsampling filter in SHVC is applied to the base layer reconstructed video, there-fore the reference video for the enhancement layer has scaling artifacts and compression artifacts. The VSR model used to perform upsampling can provide a higher quality reference for the EL. However, the traditional VSR model can’t be directly used on the SHVC as the low-resolution video used as input for the VSR upscaler is pristine but in SHVC the LR input to the upscaler is compressed by the base layer codec. High-frequency details are lost during the compression and artifacts are introduced by the block-based hybrid video coding framework. Therefore the video super-resolution models need to be modified as the compression process added artifacts to the input. In this work deartifact network (DANet) was introduced to perform the artifacts reduction and super-resolution at the same time. DANet is based on FRVSR which estimates the optical flow between frames and uses motion compensation to align local frames for the super-resolution. Our DANet performs 0.28dB and 0.81 VMAF better than the FRVSR on the PRIME7 test set with CRF23 LRC video as input. After integrating DANet to the SHVC codec, on the PRIMT7 test set, using PSNR as the metric, this VSR-integrated scalable video coding framework achieved -5.62% BD-rate reduction at the same video quality and 0.17 dB BD-PSNR quality improvement at the same bitrates compared with the original SHVC. Using VMAF as the metric, our VSR-SHVC achieved -10.01% BD-rate reduction and 0.79 BD-VMAF quality improvement

Super-Resolution in Still Images and Videos via Deep Learning

PhDThe evolution of multimedia systems and technology in the past decade has enabled production and delivery of visual content in high resolution, and the thirst for achieving higher de nition pictures with more detailed visual characteristics continues. This brings attention to a critical computer vision task for spatial up-sampling of still images and videos called super-resolution. Recent advances in machine learning, and application of deep neural networks, have resulted in major improvements in various computer vision applications. Super-resolution is not an exception, and it is amongst the popular topics that have been a ected signi cantly by the emergence of deep learning. Employing modern machine learning solutions has made it easier to perform super-resolution in both images and videos, and has allowed professionals from di erent elds to upgrade low resolution content to higher resolutions with visually appealing picture delity. In spite of that, there remain many challenges to overcome in adopting deep learning concepts for designing e cient super-resolution models. In this thesis, the current trends in super-resolution, as well as the state of the art are presented. Moreover, several contributions for improving the performance of the deep learning-based super-resolution models are described in detail. The contributions include devising theoretical approaches, as well as proposing design choices that can lead to enhancing the existing art in super-resolution. In particular, an e ective approach for training convolutional networks is proposed, that can result in optimized and quick training of complex models. In addition, speci c deep learning architectures with novel elements are introduced that can provide reduction in the complexity of the existing solutions, and improve the super-resolution models to achieve better picture quality. Furthermore, application of super-resolution for handling compressed content, and its functionality as a compression tool are studied and investigated.COGNITUS media AI software fundin