An Optimized Quantization Constraints Set for Image Restoration and its GPU Implementation

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 distributio...

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Veröffentlicht in:IEEE transactions on image processing Jg. 29; S. 6043 - 6053
Hauptverfasser: Liu, Shumin, Chen, Jiajia, Ai, Ye, Rahardja, Susanto
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Discrete cosine transforms
GPU parallelism
Graphics processing units
Image coding
Image enhancement
Image management
Image quality
Image restoration
Image retrieval
Measurement
Normal distribution
Optimization
optimized quantization constraints set
Quantization (signal)
Signal to noise ratio
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2020.2988131