Fast intra mode decision algorithm for H.263 to H.264/AVC transcoding

2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Advanced heterogeneous video transcoding

PhDVideo transcoding is an essential tool to promote inter-operability between different video communication systems. This thesis presents two novel video transcoders, both operating on bitstreams of the cur- rent H.264/AVC standard. The first transcoder converts H.264/AVC bitstreams to a Wavelet Scalable Video Codec (W-SVC), while the second targets the emerging High Efficiency Video Coding (HEVC). Scalable Video Coding (SVC) enables low complexity adaptation of compressed video, providing an efficient solution for content delivery through heterogeneous networks. The transcoder proposed here aims at exploiting the advantages offered by SVC technology when dealing with conventional coders and legacy video, efficiently reusing information found in the H.264/AVC bitstream to achieve a high rate-distortion performance at a low complexity cost. Its main features include new mode mapping algorithms that exploit the W-SVC larger macroblock sizes, and a new state-of-the-art motion vector composition algorithm that is able to tackle different coding configurations in the H.264/AVC bitstream, including IPP or IBBP with multiple reference frames. The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. This thesis proposes and evaluates several transcoding algorithms for the HEVC codec. In particular, a transcoder based on a new method that is capable of complexity scalability, trading off rate-distortion performance for complexity reduction, is proposed. Furthermore, other transcoding solutions are explored, based on a novel content-based modeling approach, in which the transcoder adapts its parameters based on the contents of the sequence being encoded. Finally, the application of this research is not constrained to these transcoders, as many of the techniques developed aim to contribute to advance the research on this field, and have the potential to be incorporated in different video transcoding architectures

Reconfigurable Computing For Video Coding

Video coding is widely used in our daily life. Due to its high computational complexity, hardware implementation is usually preferred. In this research, we investigate both ASIC hardware design approach and reconfigurable hardware design approach for video coding applications. First, we present a unified architecture that can perform Discrete Cosine Transform (DCT), Inverse Discrete Cosine Transform (IDCT), DCT domain motion estimation and compensation (DCT-ME/MC). Our proposed architecture is a Wavefront Array-based Processor with a highly modular structure consisting of 8*8 Processing Elements (PEs). By utilizing statistical properties and arithmetic operations, it can be used as a high performance hardware accelerator for video transcoding applications. We show how different core algorithms can be mapped onto the same hardware fabric and can be executed through the pre-defined PEs. In addition to the simplified design process of the proposed architecture and savings of the hardware resources, we also demonstrate that high throughput rate can be achieved for IDCT and DCT-MC by fully utilizing the sparseness property of DCT coefficient matrix. Compared to fixed hardware architecture using ASIC design approach, reconfigurable hardware design approach has higher flexibility, lower cost, and faster time-to-market. We propose a self-reconfigurable platform which can reconfigure the architecture of DCT computations during run-time using dynamic partial reconfiguration. The scalable architecture for DCT computations can compute different number of DCT coefficients in the zig-zag scan order to adapt to different requirements, such as power consumption, hardware resource, and performance. We propose a configuration manager which is implemented in the embedded processor in order to adaptively control the reconfiguration of scalable DCT architecture during run-time. In addition, we use LZSS algorithm for compression of the partial bitstreams and on-chip BlockRAM as a cache to reduce latency overhead for loading the partial bitstreams from the off-chip memory for run-time reconfiguration. A hardware module is designed for parallel reconfiguration of the partial bitstreams. The experimental results show that our approach can reduce the external memory accesses by 69% and can achieve 400 MBytes/s reconfiguration rate. Detailed trade-offs of power, throughput, and quality are investigated, and used as a criterion for self-reconfiguration. Prediction algorithm of zero quantized DCT (ZQDCT) to control the run-time reconfiguration of the proposed scalable architecture has been used, and 12 different modes of DCT computations including zonal coding, multi-block processing, and parallel-sequential stage modes are supported to reduce power consumptions, required hardware resources, and computation time with a small quality degradation. Detailed trade-offs of power, throughput, and quality are investigated, and used as a criterion for self-reconfiguration to meet the requirements set by the users

Video coding based on fractals and sparse representations

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Vídeos são sequências de imagens estáticas representando cenas em movimento. Transmitir e armazenar essas imagens sem nenhum tipo de pré-processamento necessitaria de enormes larguras de banda nos canais de comunicação e uma quantidade massiva de espaço de armazenamento. A fim de reduzir o número de bits necessários para tais dados, foram criados métodos de compressão com perda. Esses métodos geralmente consistem em um codificador e um decodificador, tal que o codificador gera uma sequência de bits que representa uma aproximação razoável do vídeo através de um formato pré-especificado e o decodificador lê essa sequência, convertendo-a novamente em uma série de imagens. A transmissão de vídeos sob restrições extremas de largura de banda tem aplicações importantes como videoconferências e circuitos fechados de televisão. Neste trabalho são abordados dois métodos destinados a essa aplicação, decomposição usando representações esparsas e compressão fractal. A ampla maioria dos codificadores tem como mecanismo principal o uso de transformações inversíveis capazes de representar imagens espacialmente suaves com poucos coeficientes não-nulos. Representações esparsas são uma generalização dessa ideia, em que a transformação tem como base um conjunto cujo número de elementos excede a dimensão do espaço vetorial onde ela opera. A projeção dos dados pode ser feita a partir de uma heurística rápida chamada Matching Pursuit. Uma abordagem combinando essa heurística com um algoritmo para gerar a base sobrecompleta por aprendizado de máquina é apresentada. Codificadores fractais representam uma aproximação da imagem como um sistema de funções iterativas. Para isso, criam e transmitem uma sequência de comandos, chamada colagem, capazes de obter uma representação da imagem na escala original dada a mesma imagem em uma escala reduzida. A colagem é criada de tal forma que, se aplicada a uma imagem inicial qualquer repetidas vezes, reduzindo sua escala antes de toda iteração, converge em uma aproximação da imagem codificada. Métodos simplificados e rápidos para a criação da colagem e uma generalização desses métodos para a compressão de vídeos são apresentados. Ao invés de construir a colagem tentando mapear qualquer bloco da escala reduzida na escala original, apenas um conjunto pequeno de blocos é considerado. O método de compressão proposto para vídeos agrupa um conjunto de quadros consecutivos do vídeo em um fractal volumétrico. A colagem mapeia blocos tridimensionais entre as escalas, considerando uma escala menor tanto no tempo quanto no espaço. Uma adaptação desse método para canais de comunicação cuja largura de banda é instável também é propostaAbstract: A video is a sequence of still images representing scenes in motion. A video is a sequence of extremely similar images separated by abrupt changes in their content. If these images were transmitted and stored without any kind of preprocessing, this would require a massive amount of storage space and communication channels with very high bandwidths. Lossy compression methods were created in order to reduce the number of bits used to represent this kind of data. These methods generally consist in an encoder and a decoder, where the encoder generates a sequence of bits that represents an acceptable approximation of the video using a certain predefined format and the decoder reads this sequence, converting it back into a series of images. Transmitting videos under extremely limited bandwidth has important applications in video conferences or closed-circuit television systems. Two different approaches are explored in this work, decomposition based on sparse representations and fractal coding. Most video coders are based on invertible transforms capable of representing spatially smooth images with few non-zero coeficients. Sparse representations are a generalization of this idea using a transform that has an overcomplete dictionary as a basis. Overcomplete dictionaries are sets with more elements in it than the dimension of the vector space in which the transform operates. The data can be projected into this basis using a fast heuristic called Matching Pursuits. A video encoder combining this fast heuristic with a machine learning algorithm capable of constructing the overcomplete dictionary is proposed. Fractal encoders represent an approximation of the image through an iterated function system. In order to do that, a sequence of instructions, called a collage, is created and transmitted. The collage can construct an approximation of the original image given a smaller scale version of it. It is created in such a way that, when applied to any initial image several times, contracting it before each iteration, it converges into an approximation of the encoded image. Simplier and faster methods for creating a collage and a generalization of these methods to video compression are presented. Instead of constructing a collage by matching any block from the smaller scale to the original one, a small subset of possible matches is considered. The proposed video encoding method creates groups of consecutive frames which are used to construct a volumetric fractal. The collage maps tridimensional blocks between the different scales, using a smaller scale in both space and time. An improved version of this algorithm designed for communication channels with variable bandwidth is presentedMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C