Motion-compensated residue preprocessing in video coding based on just-noticeable-distortion profile

Abstract

We present a motion-compensated residue signal preprocessing scheme in video coding scheme based on just-noticeable-distortion (JND) profile. Human eyes cannot sense any changes below the JND threshold around a pixel due to their underlying spatial/temporal masking properties. An appropriate (even imperfect) JND model can significantly help to improve the performance of video coding algorithms. From the viewpoint of signal compression, smaller variance of signal results in less objective distortion of the reconstructed signal for a given bit rate. In this paper, a new JND estimator for color video is devised in image-domain with the nonlinear additivity model for masking (NAMM) and is incorporated into a motion-compensated residue signal preprocessor for variance reduction toward coding quality enhancement. As the result, both perceptual quality and objective quality are enhanced in coded video at a given bit rate. A solution of adaptively determining the parameter for the residue preprocessor is also proposed. The devised technique can be applied to any standardized video coding scheme based on motion compensated prediction. It provides an extra design option for quality control, besides quantization, in contrast with most of the existing perceptually adaptive schemes which have so far focused on determination of proper quantization steps. As an example for demonstration, the proposed scheme has been implemented in the MPEG-2 TM5 coder, and achieved an average peak signal-to-noise (PSNR) increment of 0.505 dB over the twenty video sequences which have been tested. The perceptual quality improvement has been confirmed by the subjective viewing tests conducted.