Excess rate for model selection in interactive compression using belief propagation decoding

Abstract

Interactive compression refers to the problem of compressing data while sending only the part requested by the user. In this context, the challenge is to perform the extraction in the compressed domain directly. Theoretical results exist, but they assume that the true distribution is known. In practical scenarios instead, the distribution must be estimated. In this paper, we first formulate the model selection problem for interactive compression and show that it requires to estimate the excess rate incurred by mismatched decoding. Then, we propose a new expression to evaluate the excess rate of mismatched decoding in a practical case of interest: when the decoder is the beliefpropagation algorithm. We also propose a novel experimental setup to validate this closed-form formula. We show a good match for practical interactive compression schemes based on fixed-length Low-Density Parity-Check (LDPC) codes. This new formula is of great importance to perform model and rate selection.

Lossless Decoding Method of Compressed Coded Video Based on Inter-Frame Differential Background Model

“…With the progress of science and technology, people's demand for video is increasing, and image and video coding and decoding technology plays an increasingly important role in people's daily life. Due to the large amount of video data, compression coded video lossless decoding is particularly important (Bidgoli et al, 2020). Dai et al (2018) proposed a compression video codec method based on super-resolution reconstruction, which collects compressed video, performs denoising processing on the collected compressed video, performs HEVC (High Efficiency Video Coding) coding on the compressed video according to the denoising results, and reconstructs the encoded compressed video to its original size through super-resolution reconstruction method.…”