JPEG quality transcoding using neural networks trained with a perceptual error measure.

J Lazzaro; J Wawrzynek

doi:10.1162/089976699300016917

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PubMed · 9950733

JPEG quality transcoding using neural networks trained with a perceptual error measure.

J Lazzaro·J Wawrzynek

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Abstract

A JPEG Quality Transcoder (JQT) converts a JPEG image file that was encoded with low image quality to a larger JPEG image file with reduced visual artifacts, without access to the original uncompressed image. In this article, we describe technology for JQT design that takes a pattern recognition approach to the problem, using a database of images to train statistical models of the artifacts introduced through JPEG compression. In the training procedure for these models, we use a model of human visual perception as an error measure. Our current prototype system removes 32.2% of the artifacts introduced by moderate compression, as measured on an independent test database of linearly coded images using a perceptual error metric. This improvement results in an average PSNR reduction of 0.634 dB.

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Algorithms Animals Artifacts Humans Image Processing, Computer-Assisted Models, Statistical Neural Networks, Computer Pattern Recognition, Automated Visual Perception Journal Article Research Support, Non-U.S. Gov't

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J Lazzaro, J Wawrzynek. 1999-01-01. JPEG quality transcoding using neural networks trained with a perceptual error measure.. https://doi.org/10.1162/089976699300016917

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