Film grain noise modeling in advanced video coding A new technique for film grain noise extraction, modeling and synthesis is proposed and applied to the coding of high definition video in this work. The film grain noise is viewed as a part of artistic presentation by people in the movie industry. On one hand, since the film grain noise can boost the natural appearance of pictures in high definition video, it should be preserved in highfidelity video processing systems. On the other hand, video coding with film grain noise is expensive. It is desirable to extract film grain noise from the input video as a preprocessing step at the encoder and resynthesize the film grain noise and add it back to the decoded video as a postprocessing step at the decoder. Under this framework, the coding gain of the denoised video is higher while the quality of the final reconstructed video can still be well preserved. Following this idea, we present a method to remove film grain noise from imagevideo without distorting its original content. Besides, we describe a parametric model containing a small set of parameters to represent the extracted film grain noise. The proposed model generates the film grain noise that is close to the real one in terms of power spectral density and crosschannel spectral correlation. Experimental results are shown to demonstrate the efficiency of the proposed scheme.

Side Information Enhancement via Texture and Motion Activity Analysis in Distributed Video Coding This paper investigates how to exploit the artifacts constraint to enhance the quality of side information (SI) in distributed video coding (DVC). The idea originates from the observation that there are usually some regions shown as artifacts in the interpolated SI, which seriously degrade the overall performance of the DVC system, especially when the GOP size is large. Encouraged by the previous work about the backward channel in the literature, we propose a simple and effective DVC scheme based on artifact detection and removal. On one hand, texture analysis via dynamic range and local entropy is utilized to determine artifact regions in spatial domain. On the other hand, motion activity evaluation is used to determine artifact regions in temporal domain, and temporal local entropy difference is further employed to eliminate the effect of other factors like brightness variance. After the artifact blocks are detected, we recode them in intra mode and replace the colocated ones at the decoder side so that the quality of SI is enhanced. Experimental results demonstrate that the proposed SI quality enhancement technique is effective.