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Dictionary Learning-based, Directional and Optimized Prediction for Lenslet Image Coding This publication appears in: IEEE Transactions on Circuits and Systems for Video Technology Authors: R. Zhong, I. Schiopu, B. Cornelis, S. Lu, J. Yuan and A. Munteanu Volume: 29 Issue: 4 Pages: 1116-1129 Publication Date: Apr. 2018
Abstract: In this paper, a novel approach to encode lenslet (LL) images is proposed. The method departs from traditional block-based coding structures and employs a hexagonal-shaped pixel cluster, called macro-pixel, as an elementary coding unit. A novel prediction mode based on dictionary learning is proposed, whereby macro-pixels are represented by a sparse linear combination of atoms from a generic dictionary. Additionally, an optimized linear prediction mode and a directional prediction mode specifically designed for macro-pixels are proposed. Rate-distortion optimization is utilized to select the best intra prediction mode for each macro-pixel. Experimental results on the light field image data set show that the proposed coding system outperforms HEVC and the state-of-the-art in LL image coding with an average peak signal to noise ratio gain of 3.33 and 1.41 dB, respectively, and with rate savings of 67.13% and 34.30%, respectively.
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