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An Optimized Quantization Constraints Set for Image Restoration and its GPU Implementation
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DOI：10.1109/TIP.2020.2988131.png)
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摘要
En 中文
This paper presents a novel optimized quantization constraint set, acting as an add-on to existing DCT-based image restoration algorithms. The constraint set is created based on generalized Gaussian distribution which is more accurate than the commonly used uniform, Gaussian or Laplacian distributions when modeling DCT coefficients. More importantly, the proposed constraint set is optimized for individual input images and thus it is able to enhance image quality significantly in terms of signal-to-noise ratio. Experimental results indicate that the signal-to-noise ratio is improved by at least 6.78% on top of the existing state-of-the-art methods, with a corresponding expense of only 0.38% in processing time. The proposed algorithm has also been implemented in GPU, and the processing speed increases further by 20 times over that of CPU implementation. This makes the algorithm well suited for fast image retrieval in security and quality monitoring system.
Keyword:
Discrete cosine transforms
Image restoration
Quantization (signal)
Graphics processing units
Signal to noise ratio
Optimization
Image coding
Image restoration
optimized quantization constraints set
GPU parallelism
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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