Complexity-based rate distortion optimization with perceptual tuning for scalable video coding

This paper has proposed a content adaptive rate-distortion optimization scheme for scalable video coding. To adaptively determine the tradeoff between texture and motion coding, the scheme first makes use of spatial and temporal complexities to adjust the Lagrangian multiplier for all the macroblocks in a frame. After that, the Lagrangian multiplier for every macroblock of the frame is further refined using local perceptual cues. To ensure optimal local selection of the Lagrangian multiplier, several characteristics of human visual system have been taken into account to guide the adjustment. Experiments have shown that the proposed scheme could effectively adapt the adjustment of Lagrangian multiplier to video contents and the subjective quality of the reconstructed frames could be improved without compromising PSNR.