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• W2765548737 abstract "Lossy image compression methods always introduce various unpleasant artifacts into the compressed results, especially at low bit-rates. In recent years, many effective soft decoding methods for JPEG compressed images have been proposed. However, to the best of our knowledge, very few works have been done on soft decoding of JPEG 2000 compressed images. Inspired by the outstanding performance of Convolution Neural Network (CNN) in various computer vision tasks, we presents a soft decoding method for JPEG 2000 by using multiple bit-rate-driven deep CNNs. More specifically, in training stage, we train a series of deep CNNs using lots of high quality training images and the corresponding JPEG 2000 compressed images at different coding bit-rates. In testing stage, for an input compressed image, the CNN trained with the nearest coding bit-rate is selected to perform soft decoding. Extensive experiments demonstrate the effectiveness of the presented soft decoding framework, which greatly improves the visual quality and objective scores of JPEG 2000 compressed images." @default.
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• W2765548737 date "2017-07-01" @default.
• W2765548737 modified "2023-10-18" @default.
• W2765548737 title "Soft decoding of JPEG 2000 compressed images using bit-rate-driven deep convolutional neural networks" @default.
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• W2765548737 doi "https://doi.org/10.1109/icinfa.2017.8079020" @default.
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