Side Information Generation Scheme Based on Coefficient Matrix Improvement Model in Transform Domain Distributed Video Coding

Abstract

In order to effectively improve the quality of side information in distributed video coding, we propose a side information generation scheme based on a coefficient matrix improvement model. The discrete cosine transform coefficient bands of the Wyner–Ziv frame at the encoder side are divided into entropy coding coefficient bands and distributed video coding coefficient bands, and then the coefficients of entropy coding coefficient bands are sampled, which are divided into sampled coefficients and unsampled coefficients. For sampled coefficients, an adaptive arithmetic encoder is used for lossless compression. For unsampled coefficients and the coefficients of distributed video coding coefficient bands, the low density parity check accumulate encoder is used to calculate the parity bits, which are stored in the buffer and transmitted in small amount upon decoder request. At the decoder side, the optical flow method is used to generate the initial side information, and the initial side information is improved according to the sampled coefficients by using the coefficient matrix improvement model. The experimental results demonstrate that the proposed side information generation scheme based on the coefficient matrix improvement model can effectively improve the quality of side information, and the quality of the generated side information is improved by about 0.2–0.4 dB, thereby improving the overall performance of the distributed video coding system.

Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information

“…With the SIF, we can use them to calculate the proportion of the significant coefficients of each block. Considering that the SIF are generated on the reconstruction side, we need to use the feedback channel to transmit the obtained proportion of the significant coefficients to the sampling side, in which the feedback channel is a very common channel in the distributed video coding system [12,13]. In this paper, the feedback channel is used to feed back the obtained proportion of each block to the sampling side, that is, each block only needs to feed back a proportion value to the sampling side.…”

“…On the basis of the above consideration, we propose an adaptive block-based compressed video sensing (ABCVS) scheme to comprehensively estimate the measurement number of each block by using the saliency detection in the measurement domain and the side information [12,13] generated on the reconstruction side. Firstly, on the sampling side, the saliency value of each block of the current frame is estimated in the measurement domain.…”

Source Symbol Purging-Based Distributed Conditional Arithmetic Coding

“…DSC is based on two important theorems: the first is the Slepian-Wolf theorem [1], which proves that when data is losslessly compressed, independent coding and joint coding of related sources are equally effective; the second is the Wyner-Ziv theorem [2], which establishes the foundation for lossy DSC schemes. Today, DSC has led to achievements in many practical applications, such as: distributed video coding [3], hyperspectral image compression [4], multi-view video coding [5], wireless sensor network communication [6], image authentication [7], biometric information recognition [8] and so on.…”