Closed loop optimization of image coding using subjective error criteria

This paper proposes a closed-loop optimization framework to improve image coding efficiency by searching DCT coefficients at the equivalent subjective quality to the original coding result. The proposed framework shares a basic idea currently adopted in speech coding that searches optimal codes in a closed-loop operation, evaluating the coded signal with perceptually weighted mean square error. To evaluate the perceptual quality in image coding, we introduce the masked PSNR that accounts for the masking effects, by which we apply the stepwise removal of subjectively negligible DCT coefficients. The result justifies the effectiveness of the proposed framework