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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 images is proposed. The method departs from traditional blockbased coding structures and employs a hexagonal-shaped pixel cluster, called macro-pixel, as 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. Ratedistortion optimization is utilized to select the best intra prediction mode for each macro-pixel. Experimental results on the EPFL light field image dataset show that the proposed coding system outperforms HEVC and the state-of-the-art in lenslet image coding with an average PSNR gain of 3.33 dB and 1.41 dB, respectively, and with rate savings of 67.13% and 34.30%, respectively.

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Miss Rui Zhong

+32 (0)02 629 168

rzhong@etrovub.be

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Dr. Ionut Schiopu

+32 (0)02 629 169

ischiopu@etrovub.be

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Dr. Ir. Bruno Cornelis

+32 (0)02 629 167

bcorneli@etrovub.be

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Mr. Shao-Ping Lu

+32 (0)02 629 168

slu@etrovub.be

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Prof. Dr. Ir. Adrian Munteanu

+32 (0)02 629 168

acmuntea@etrovub.be

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