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

Neural Comput. 1999 Jan 1;11(1):267-96. doi: 10.1162/089976699300016917.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Animals
  • Artifacts*
  • Humans
  • Image Processing, Computer-Assisted*
  • Models, Statistical
  • Neural Networks, Computer*
  • Pattern Recognition, Automated
  • Visual Perception / physiology*