Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142307
Title: Light field compression with disparity-guided sparse coding based on structural key views
Authors: Chen, Jie
Hou, Junhui
Chau, Lap-Pui
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Chen, J., Hou, J., & Chau, L.-P. (2018). Light field compression with disparity-guided sparse coding based on structural key views. IEEE Transactions on Image Processing, 27(1), 314-324. doi:10.1109/TIP.2017.2750413
Journal: IEEE Transactions on Image Processing
Abstract: Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by an LF image as compared with a conventional 2D image of similar spatial dimension. Hence, the compression of LF data becomes a vital part of its application. In this paper, we propose an LF codec with disparity guided Sparse Coding over a learned perspective-shifted LF dictionary based on selected Structural Key Views (SC-SKV). The sparse coding is based on a limited number of optimally selected SKVs; yet the entire LF can be recovered from the coding coefficients. By keeping the approximation identical between encoder and decoder, only the residuals of the non-key views, disparity map, and the SKVs need to be compressed into the bit stream. An optimized SKV selection method is proposed such that most LF spatial information can be preserved. To achieve optimum dictionary efficiency, the LF is divided into several coding regions, over which the reconstruction works individually. Experiments and comparisons have been carried out over benchmark LF data set, which show that the proposed SC-SKV codec produces convincing compression results in terms of both rate-distortion performance and visual quality compared with Joint Exploration Model: with 37.9% BD-rate reduction and 1.17-dB BD-PSNR improvement achieved on average, especially with up to 6-dB improvement for low bit rate scenarios.
URI: https://hdl.handle.net/10356/142307
ISSN: 1057-7149
DOI: 10.1109/TIP.2017.2750413
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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