Quantization noise removal for optimal transform decoding

This paper examines the relationship between quantization noise removal and the variational problem. Traditional transformed and quantized image restoration techniques cannot prevent parasitic effects due to quantization noise. We propose a new method, involving a priori assumptions on the solution and knowledge of the coder (transformation and quantization) to account for effects due to quantization noise. This technique, called MORPHE, can be viewed as an inverse problem with optimization of the transform/quantization/decoding structure. This leads to the study of different ways to solve the constrained optimization problem. Experiments using this nonlinear inverse dynamic filtering demonstrate PSNR gains over standard linear inverse filtering as well as appreciable visual improvements