A new joint Rate-distortion Optimization model based on texture and mean-error factors

Abstract

In this paper, a new joint Rate-distortion Optimization model based on texture and mean-error factors (TMJRDM) is proposed. Texture factor implies the subjective factor, giving the texture similarity measure between the original image and the target image. At the same time, mean-error factor gives the objective measure. The joint weighted value of texture and mean-error factors is calculated as the distortion metric, which balances the subjective and objective quality. Experimental results show that our proposed algorithm improves the subjective quality of the reconstructed images without obvious degradation of the objective quality.