A perceptualbased approach to bit allocation for H.264 encoder Since the ultimate receivers of encoded video are human eyes, the characteristics of human visual system should be taken into consideration in the design of bit allocation to improve the perceptual video quality. In this paper, we incorporate the structural similarity index as a distortion metric and propose a novel ratedistortion model to characterize the relationship between rate and the structural similarity index. Based on the model, we develop an optimum bit allocation and rate control scheme for H.264 encoders. Experimental results show that up to 25% bitrate reduction over the JM reference software can be achieved. Subjective evaluation further confirms that the proposed scheme preserves more structural information and improves the perceptual quality of the encoded video.

SSIM based perceptual distortion rate optimization coding The current rate distortion optimization (RDO) coding schemes usually use the Sum of Absolute Difference (SAD) or Sum of Square Difference (SSD) as the distortion metric. However, neither SAD nor SSD correlates with the human visual system (HVS) very well. To develop a perceptual distortion based video encoder, we employ the Structural Similarity (SSIM) Index as the distortion metric and propose a SSIM based Lagrangian perceptual distortion rate optimization (PDRO) method in this paper. Furthermore, to adapt the different input sequences dynamically, we present an adaptive Lagrange multiplier selection scheme based on the properties of the input sequences. By modeling the transformed residuals with Laplace distribution, the statistical SSIM and rate models are deduced to obtain the adaptive Lagrange multiplier. Extensive experiments show that the proposed scheme can achieve better perceptual distortion rate performance and provide better visual quality than the SADSSD based RDO coding scheme.