Codificação compatível de vídeo 3D com o algoritmo HEVC

Esta dissertação apresenta um trabalho sobre codificação de vídeo 3D compatível com vídeo 2D. Tem por base o desenvolvimento de um método para melhorar, no descodificador, a reconstrução de uma vista subamostrada resultante de uma transmissão simulcast usando a norma de codificação de vídeo H.265 (informalmente denominada de High Efficiency Video Coding (HEVC)). Apesar de manter a compatibilidade com vídeo 2D a transmissão simulcast normalmente requer uma taxa de transmissão elevada. Na ausência de ferramentas de codificação 3D adequadas é possível reduzir a taxa de transmissão utilizando compressão assimétrica do vídeo, onde a vista base é codificada com a resolução espacial original, enquanto que a vista auxiliar é codificada com uma resolução espacial menor, sendo sobreamostrada no descodificador. O método desenvolvido visa melhorar a vista auxiliar sobreamostrada no descodificador utilizando informação dos detalhes da vista base, ou seja, as componentes de alta frequência. Este processo depende de transformadas Afim para realizar um mapeamento geométrico entre a informação de alta frequência da vista base de resolução completa e a vista auxiliar de menor resolução. Adicionalmente, de modo a manter a continuidade do conteúdo da imagem entre regiões, evitando artefatos de blocos, o mapeamento utiliza uma malha de triangulação da vista auxiliar aplicado à imagem de detalhes obtida a partir da vista base. A técnica proposta é comparada com um método de estimação de disparidade por correspondência de blocos, sendo que os resultados mostram que para algumas sequências a técnica desenvolvida melhora não só a qualidade objetiva (PSNR) até 2.2 dB, mas também a qualidade subjetiva, para a mesma taxa de compressão global.

Contributions to HEVC Prediction for Medical Image Compression

Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this dissertation, two different approaches to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, were studied and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a first approach, the use of geometric transformations to perform inter-slice prediction was investigated. For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction, that exploits inter-slice redundancy was proposed to extend the current HEVC lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when compared with DICOM recommended encoders, and 13.7% when compared with standard HEVC.

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Image and video compression play a major role in the world today, allowing the storage and transmission of large multimedia content volumes. However, the processing of this information requires high computational resources, hence the improvement of the computational performance of these compression algorithms is very important. The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based compression algorithm for multimedia contents, namely images, achieving high compression ratios, maintaining good image quality, Rodrigues et al. [2008]. However, in comparison with other existing algorithms, this algorithm takes some time to execute. Therefore, two parallel implementations for GPUs were proposed by Ribeiro [2016] and Silva [2015] in CUDA and OpenCL-GPU, respectively. In this dissertation, to complement the referred work, we propose two parallel versions that run the MMP algorithm in CPU: one resorting to OpenMP and another that converts the existing OpenCL-GPU into OpenCL-CPU. The proposed solutions are able to improve the computational performance of MMP by 3 and 2:7 , respectively. The High Efficiency Video Coding (HEVC/H.265) is the most recent standard for compression of image and video. Its impressive compression performance, makes it a target for many adaptations, particularly for holoscopic image/video processing (or light field). Some of the proposed modifications to encode this new multimedia content are based on geometry-based disparity compensations (SS), developed by Conti et al. [2014], and a Geometric Transformations (GT) module, proposed by Monteiro et al. [2015]. These compression algorithms for holoscopic images based on HEVC present an implementation of specific search for similar micro-images that is more efficient than the one performed by HEVC, but its implementation is considerably slower than HEVC. In order to enable better execution times, we choose to use the OpenCL API as the GPU enabling language in order to increase the module performance. With its most costly setting, we are able to reduce the GT module execution time from 6.9 days to less then 4 hours, effectively attaining a speedup of 45 .