A Spectrally Adaptive Noise Filling Tool for Perceptual Transform Coding of Still Images

Modern perceptual image coders reach impressively high subjective quality even at low bit-rates but tend to denoise or “detexturize” the coded pictures. Traditionally, two independent parametric approaches, known as texture and film grain synthesis, have been applied in the spatial domain as pre and post-processors around the codec to counteract such effects. In this work, a unified alternative, operating directly within the spectral domain of conventional transform codecs with tight coupling to the transform coefficient quantizer, is proposed. Due to its design, this spectrally adaptive noise filling tool (SANFT) enables highly input adaptive realizations by reusing the coder's existing optimized spatial and spectral partitioning algorithms. Formal subjective evaluation in the context of a main still picture” High Emciency Video Coding (HEVC) implementation confirms the benefit of the proposal.