Adaptive in-loop noise-filtered prediction for High Efficiency Video Coding

Abstract

Compression of noisy image sequences is a hard challenge in video coding. Especially for high quality compression the preprocessing of videos is not possible, as it decreases the objective quality of the videos. In order to overcome this problem, this paper presents an in-loop denoising framework for efficient medium to high fidelity compression of noisy video data. It is shown that using low complexity in-loop noise estimation and noise filtering as well as adaptive selection of the denoised inter frame predictors can improve the compression performance. The proposed algorithm for adaptive selection of the denoised predictor is based on the actual HEVC reference model. The different inter frame prediction modes within the current HEVC reference model are exploited for adaptive selection of denoised prediction by transmission of some side information in combination with decoder side estimation for denoised prediction. The simulation results show considerable gains using the proposed in-loop denoising framework with adaptive selection. In addition the theoretical bounds for the compression efficiency, if we could perfectly estimate the adaptive selection of the denoised prediction in the decoder, are shown in the simulation results.