Mode Decision-Based Algorithm for Complexity Control in H.264/AVC

The latest H.264/AVC video coding standard achieves high compression rates in exchange for high computational complexity. Nowadays, however, many application scenarios require the encoder to meet some complexity constraints. This paper proposes a novel complexity control method that relies on a hypothesis testing that can handle time-variant content and target complexities. Specifically, it is based on a binary hypothesis testing that decides, on a macroblock basis, whether to use a low-or a high-complexity coding model. Gaussian statistics are assumed so that the probability density functions involved in the hypothesis testing can be easily adapted. The decision threshold is also adapted according to the deviation between the actual and the target complexities. The proposed method is implemented on the H.264/AVC reference software JM10.2 and compared with a state-of-the-art method. Our experimental results prove that the proposed method achieves a better trade-off between complexity control and coding efficiency. Furthermore, it leads to a lower deviation from the target complexity.This work has been partially supported by the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad

Algorithms & implementation of advanced video coding standards

Advanced video coding standards have become widely deployed coding techniques used in numerous products, such as broadcast, video conference, mobile television and blu-ray disc, etc. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every five years. However, the trend also has brought many problems, such as, dramatically increased computational complexity, co-existing multiple standards and gradually increased development time. To solve the above problems, this thesis intends to investigate efficient algorithms for the latest video coding standard, H.264/AVC. Two aspects of H.264/AVC standard are inspected in this thesis: (1) Speeding up intra4x4 prediction with parallel architecture. (2) Applying an efficient rate control algorithm based on deviation measure to intra frame. Another aim of this thesis is to work on low-complexity algorithms for MPEG-2 to H.264/AVC transcoder. Three main mapping algorithms and a computational complexity reduction algorithm are focused by this thesis: motion vector mapping, block mapping, field-frame mapping and efficient modes ranking algorithms. Finally, a new video coding framework methodology to reduce development time is examined. This thesis explores the implementation of MPEG-4 simple profile with the RVC framework. A key technique of automatically generating variable length decoder table is solved in this thesis. Moreover, another important video coding standard, DV/DVCPRO, is further modeled by RVC framework. Consequently, besides the available MPEG-4 simple profile and China audio/video standard, a new member is therefore added into the RVC framework family. A part of the research work presented in this thesis is targeted algorithms and implementation of video coding standards. In the wide topic, three main problems are investigated. The results show that the methodologies presented in this thesis are efficient and encourage

Algorithms for complexity management in video coding

Nowadays, the applications based on video services are becoming very popular, e.g., the transmission of video sequences over the Internet or mobile networks, or the increasingly common use of the High Definition (HD) video signals in television or Blu-Ray systems. Thanks to this popularity of video services, video coding has become an essential tool to send and store digital video sequences. The standardization organizations have developed several video coding standards, being the most recent H.264/AVC and HEVC. Both standards achieve great results compressing the video signal by virtue of a set of spatio-temporal predictive techniques. Nevertheless, the efficacy of these techniques comes in exchange for a high increase in the computational cost of the video coding process. Due to the high complexity of these standards, a variety of algorithms attempting to control the computational burden of video coding have been developed. The goal of these algorithms is to control the coder complexity, using a specific amount of coding resources while keeping the coding efficiency as high as possible. In this PhD Thesis, we propose two algorithms devoted to control the complexity of the H.264/AVC and HEVC standards. Relying on the statistical properties of the video sequences, we will demonstrate that the developed methods are able to control the computational burden avoiding relevant losses in coding efficiency. Moreover, our proposals are designed to adapt their behavior according to the video content, as well as to different target complexities. The proposed methods have been thoroughly tested and compared with other state-of-the-art proposals for a variety of video resolutions, video sequences and coding configurations. The obtained results proved that our methods outperform other approaches and revealed that they are suitable for practical implementations of coding standards, where the computational complexity becomes a key feature for a proper design of the system.En la actualidad, la popularidad de las aplicaciones basadas en servicios de vídeo, como su transmisión sobre Internet o redes móviles, o el uso de la alta definición (HD) en sistemas de televisión o Blu-Ray, ha hecho que la codificación de vídeo se haya convertido en una herramienta imprescindible para poder transmitir y almacenar eficientemente secuencias de vídeo digitalizadas. Los organismos de estandarización han desarrollado diversos estándares de codificación de vídeo, siendo los más recientes H.264/AVC y HEVC. Ambos consiguen excelentes resultados a la hora de comprimir señales de vídeo, gracias a una serie de técnicas predictivas espacio-temporales. Sin embargo, la eficacia de estas técnicas tiene como contrapartida un considerable aumento en el coste computacional del proceso de codificación. Debido a la alta complejidad de estos estándares, se han desarrollado una gran cantidad de métodos para controlar el coste computacional del proceso de codificación. El objetivo de estos métodos es controlar la complejidad del codificador, utilizando para ello una cantidad de recursos específica mientras procuran maximizar la eficiencia del sistema. En esta Tesis, se proponen dos algoritmos dedicados a controlar la complejidad de los estándares H.264/AVC y HEVC. Apoyándose en las propiedades estadísticas de las secuencias de vídeo, demostraremos que los métodos desarrollados son capaces de controlar la complejidad sin incurrir en graves pérdidas de eficiencia de codificación. Además, nuestras propuestas se han diseñado para adaptar su funcionamiento al contenido de la secuencia de vídeo, así como a diferentes complejidades objetivo. Los métodos propuestos han sido ampliamente evaluados y comparados con otros sistemas del estado de la técnica, utilizando para ello una gran variedad de secuencias, resoluciones, y configuraciones de codificación, demostrando que alcanzan resultados superiores a los métodos con los que se han comparado. Adicionalmente, se ha puesto de manifiesto que resultan adecuados para implementaciones prácticas de los estándares de codificación, donde la complejidad computacional es un parámetro clave para el correcto diseño del sistema.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Fernando Jaureguizar Núñez.- Secretario: Iván González Díaz.- Vocal: Javier Ruiz Hidalg

Low complexity in-loop perceptual video coding

The tradition of broadcast video is today complemented with user generated content, as portable devices support video coding. Similarly, computing is becoming ubiquitous, where Internet of Things (IoT) incorporate heterogeneous networks to communicate with personal and/or infrastructure devices. Irrespective, the emphasises is on bandwidth and processor efficiencies, meaning increasing the signalling options in video encoding. Consequently, assessment for pixel differences applies uniform cost to be processor efficient, in contrast the Human Visual System (HVS) has non-uniform sensitivity based upon lighting, edges and textures. Existing perceptual assessments, are natively incompatible and processor demanding, making perceptual video coding (PVC) unsuitable for these environments. This research allows existing perceptual assessment at the native level using low complexity techniques, before producing new pixel-base image quality assessments (IQAs). To manage these IQAs a framework was developed and implemented in the high efficiency video coding (HEVC) encoder. This resulted in bit-redistribution, where greater bits and smaller partitioning were allocated to perceptually significant regions. Using a HEVC optimised processor the timing increase was < +4% and < +6% for video streaming and recording applications respectively, 1/3 of an existing low complexity PVC solution. Future work should be directed towards perceptual quantisation which offers the potential for perceptual coding gain

Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods