A new framework for noise-resistant video compression using motion-compensated prediction

This paper introduces a new framework for video compression. The proposed method considers noise directly in the video sequence and seeks the optimal compression ratio and video quality. Compression is achieved by eliminating the spatial and temporal redundancies found in the intensity and motion fields of the video. Processing is performed in blocks of N frames stored in a video buffer. Encoder and decoder are synchronized prior to the transmission of a new block. A reference frame is chosen from each block and encoded before transmission. Spatial redundancies in the intensity domain are reduced by a wavelet filter. The pixel-motion field between the reference frame and other frames in a block is evaluated using a Kalman filter that estimates the pixel motion in the presence of noise. Video frames are predicted from the reference frame and the corresponding motion field. Prediction errors, motion vectors and the reference frame are compressed in wavelet domain before transmission. The compression system includes quantization and entropy coding