Very fast image and video codecs are a pursued goal both in the academia and the industry. This paper presents a complexity scalable and parallel bitplane coding engine for wavelet-based image codecs. The proposed met...
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Very fast image and video codecs are a pursued goal both in the academia and the industry. This paper presents a complexity scalable and parallel bitplane coding engine for wavelet-based image codecs. The proposed method processes the coefficients in parallel, suiting hardware architectures based on vector instructions. Our previous work is extended with a mechanism that provides complexity scalability to the system. Such a feature allows the coder to regulate the throughput achieved at the expense of slightly penalizing compression efficiency. Experimental results suggests that, when using the fastest speed, the method almost doubles the throughput of our previous engine while penalizing compression efficiency by about 10%
Modern image and video compression standards employ computationally intensive algorithms that provide advanced features to the coding system. Current standards often need to be implemented in hardware or using expensi...
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Modern image and video compression standards employ computationally intensive algorithms that provide advanced features to the coding system. Current standards often need to be implemented in hardware or using expensive solutions to meet the real-time requirements of some environments. Contrarily to this trend, this paper proposes an end-to-end codec architecture running on inexpensive Graphics Processing Units (GPUs) that is based on, though not compatible with, the JPEG2000 international standard for image and video compression. When executed in a commodity Nvidia GPU, it achieves real time processing of 12K video. The proposed S/W architecture utilizes four CUDA kernels that minimize memory transfers, use registers instead of shared memory, and employ a double-buffer strategy to optimize the streaming of data. The analysis of throughput indicates that the proposed codec yields results at least superior on average to those achieved with JPEG2000 implementations devised for CPUs, and approximately superior to those achieved with hardwired solutions of the HEVC/H.265 video compression standard.
Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fi...
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Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo.
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