We propose a new channel adaptive distributed video coding algorithm, which is adaptive to time-varying available bitrate and packet loss ratio. The proposed method controls the quantization parameter according to channel condition of especially error-prone mobile channel. Simulation shows that the proposed algorithm outperforms the conventional rate-control-only algorithm.

This letter proposes a new algorithm of refining the quantization parameter in H.264 real-time encoding. In the H.264 encoding, the quantization parameter computed according to the quadratic rate model is not accurate in meeting the target bit rate. In order to make the actual encoded bit rate closer to the target bit rate, ρ-domain rate model is introduced in our proposed quantization parameter refinement algorithm. Simulation results show that the proposed algorithm achieves obvious gain in PSNR and has stabler encoded bit rate compared to Jiang's algorithm.

This paper introduces an adaptive low complexity pre-processing filter to improve the coding performance of seriously degraded video sequences that is caused by the additive noise. The additive noise leads to a decrease in coding performance due to the high frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given quantization step size. In order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.

In this paper, we propose an efficient requantization method for transcoding of MPEG video. Transcoding is the process of converting a compressed video format to another different compressed video format. We propose an simple and efficient transcoding by requantization in which MPEG coded video at high bit-rate is converted into MPEG bitstream at lower bit-rate. To reduce a image quality degradation, we use HVS (Human Visual System). By using this effect, the part of image in high activity region is coarsely quantized without seriously degrading the image quality. Experimental results show that the proposed method can provide good performance.