Reducing the complexity of a multiview H.264/AVC and HEVC hybrid architecture

With the advent of 3D displays, an efficient encoder is required to compress the video information needed by them. Moreover, for gradual market acceptance of this new technology, it is advisable to offer backward compatibility with existing devices. Thus, a multiview H.264/Advance Video Coding (AVC) and High Efficiency Video Coding (HEVC) hybrid architecture was proposed in the standardization process of HEVC. However, it requires long encoding times due to the use of HEVC. With the aim of tackling this problem, this paper presents an algorithm that reduces the complexity of this hybrid architecture by reducing the encoding complexity of the HEVC views. By using Na < ve-Bayes classifiers, the proposed technique exploits the information gathered in the encoding of the H.264/AVC view to make decisions on the splitting of coding units in HEVC side views. Given the novelty of the proposal, the only similar work found in the literature is an unoptimized version of the algorithm presented here. Experimental results show that the proposed algorithm can achieve a good tradeoff between coding efficiency and complexity

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

Symmetry considerations play a key role in modern science, and any differentiable symmetry of the action of a physical system has a corresponding conservation law. Symmetry may be regarded as reduction of Entropy. This work focuses on reducing the computational complexity of modern video coding standards by using the maximum entropy principle. The high computational complexity of the coding unit (CU) size decision in modern video coding standards is a critical challenge for real-time applications. This problem is solved in a novel approach considering CU termination, skip, and normal decisions as three-class making problems. The maximum entropy model (MEM) is formulated to the CU size decision problem, which can optimize the conditional entropy; the improved iterative scaling (IIS) algorithm is used to solve this optimization problem. The classification features consist of the spatio-temporal information of the CU, including the rate–distortion (RD) cost, coded block flag (CBF), and depth. For the case analysis, the proposed method is based on High Efficiency Video Coding (H.265/HEVC) standards. The experimental results demonstrate that the proposed method can reduce the computational complexity of the H.265/HEVC encoder significantly. Compared with the H.265/HEVC reference model, the proposed method can reduce the average encoding time by 53.27% and 56.36% under low delay and random access configurations, while Bjontegaard Delta Bit Rates (BD-BRs) are 0.72% and 0.93% on average

Bayesian adaptive algorithm for fast coding unit decision in the High Efficiency Video Coding (HEVC) standard

The latest High Efficiency Video Coding standard (HEVC) provides a set of new coding tools to achieve a significantly higher coding efficiency than previous standards. In this standard, the pixels are first grouped into Coding Units (CU), then Prediction Units (PU), and finally Transform Units (TU). All these coding levels are organized into a quadtree-shaped arrangement that allows highly flexible data representation; however, they involve a very high computational complexity. In this paper, we propose an effective early CU depth decision algorithm to reduce the encoder complexity. Our proposal is based on a hierarchical approach, in which a hypothesis test is designed to make a decision at every CU depth, where the algorithm either produces an early termination or decides to evaluate the subsequent depth level. Moreover, the proposed method is able to adaptively estimate the parameters that define each hypothesis test, so that it adapts its behavior to the variable contents of the video sequences. The proposed method has been extensively tested, and the experimental results show that our proposal outperforms several state-of-the-art methods, achieving a significant reduction of the computational complexity (36.5% and 38.2% average reductions in coding time for two different encoder configurations) in exchange for very slight losses in coding performance (1.7% and 0.8% average bit rate increments).This work has been partially supported by the National Grant TEC2014-53390-P of the Spanish Ministry of Economy and Competitiveness

Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

International audienceThe scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC), which introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels, including merge mode, inter2Nx2N, inter2NxN, interNx2N, interNxN, in-ter2NxnU, inter2NxnD, internLx2N and internRx2N, intra modes and inter-layer reference (ILR) mode. This can obtain the highest coding efficiency, but also result in very high coding complexity. Therefore, it is crucial to improve coding speed while maintaining coding efficiency. In this research, we have proposed a new depth level and inter mode prediction algorithm for quality SHVC. First, the depth level candidates are predicted based on inter-layer correlation, spatial correlation and its correlation degree. Second, for a given depth candidate, we divide mode prediction into square and non-square mode predictions respectively. Third, in the square mode prediction, ILR and merge modes are predicted according to depth correlation, and early terminated whether residual distribution follows a Gaussian distribution. Moreover, ILR mode, merge mode and inter2Nx2N are early terminated based on significant differences in Rate Distortion (RD) costs. Fourth, if the early termination condition cannot be satisfied, non-square modes are further predicted based on significant differences in expected values of residual coefficients. Finally, inter-layer and spatial correlations are combined with residual distribution to examine whether to early terminate depth selection. Experimental results have demonstrated that, on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%

CTU Depth Decision Algorithms for HEVC: A Survey

High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64x64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1(AV1)

Visual Saliency Estimation Via HEVC Bitstream Analysis

Abstract Since Information Technology developed dramatically from the last century 50's, digital images and video are ubiquitous. In the last decade, image and video processing have become more and more popular in biomedical, industrial, art and other fields. People made progress in the visual information such as images or video display, storage and transmission. The attendant problem is that video processing tasks in time domain become particularly arduous. Based on the study of the existing compressed domain video saliency detection model, a new saliency estimation model for video based on High Efficiency Video Coding (HEVC) is presented. First, the relative features are extracted from HEVC encoded bitstream. The naive Bayesian model is used to train and test features based on original YUV videos and ground truth. The intra frame saliency map can be achieved after training and testing intra features. And inter frame saliency can be achieved by intra saliency with moving motion vectors. The ROC of our proposed intra mode is 0.9561. Other classification methods such as support vector machine (SVM), k nearest neighbors (KNN) and the decision tree are presented to compare the experimental outcomes. The variety of compression ratio has been analysis to affect the saliency

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

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

Novi algoritam za kompresiju seizmičkih podataka velike amplitudske rezolucije

Renewable sources cannot meet energy demand of a growing global market. Therefore, it is expected that oil & gas will remain a substantial sources of energy in a coming years. To find a new oil & gas deposits that would satisfy growing global energy demands, significant efforts are constantly involved in finding ways to increase efficiency of a seismic surveys. It is commonly considered that, in an initial phase of exploration and production of a new fields, high-resolution and high-quality images of the subsurface are of the great importance. As one part in the seismic data processing chain, efficient managing and delivering of a large data sets, that are vastly produced by the industry during seismic surveys, becomes extremely important in order to facilitate further seismic data processing and interpretation. In this respect, efficiency to a large extent relies on the efficiency of the compression scheme, which is often required to enable faster transfer and access to data, as well as efficient data storage. Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, this work explores a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. It is proposed to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, this work modifies almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. In addition, optimized block selection, reduced intra prediction modes, and flexible motion estimation are tested to adapt to the structure of seismic data. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. There is no similar work in the field of the seismic data compression that uses the HEVC as a base codec setting. Thus, a specific codec design has been tailored which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-tonoise- ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a proposed configurations, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, an optimized encoder is proposed in this work. It reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses. As a side contribution of this work, HEVC is analyzed within all of its functional units, so that the presented work itself can serve as a specific overview of methods incorporated into the standard