A Study on Compression Rate Bounds in Distributed Video Coding Based on Correlation Noise Models

In a distributed video coding problem, use of a correct model for the correlation noise plays a significant role in improving the decoding performance and consequently in providing higher coding efficiency. In this work, we first study the predictive and additive correlation noise models at the DCT coefficient band level for transform-domain distributed video coding. Then, bounds on compression rates for encoding the quantized DCT coefficient band are obtained for both the correlation noise models. We then investigate how the distribution of the DCT coefficient bands in each WZ frame affects the compression rate bound in each correlation noise model. It is shown that for the DCT coefficient bands being non-uniformly distributed, the compression rate bound in the additive correlation noise model lower than that in the predictive one. Moreover, it is shown that selecting a wrong correlation model leads to compression rate loss in the decoder. The simulation results are provided to validate the theoretical investigation.