Light Field Image Compression Based on Bi-Level View Compensation with Rate-Distortion Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

18 Scopus Citations
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Original languageEnglish
Pages (from-to)517-530
Journal / PublicationIEEE Transactions on Circuits and Systems for Video Technology
Issue number2
Online published6 Feb 2018
Publication statusPublished - Feb 2019


Compared with conventional RGB images, light field images (LFIs) contain richer scene information, which allows a wide range of interesting applications. However, such additional information is obtained at the cost of generating substantially more data, which poses challenges to both data storage and transmission. In this paper, we propose a new hybrid framework for effective compression of LFIs. The proposed framework takes the particular characteristics of LFIs into account so that the inter- and intra-view correlations of LFIs can be more efficiently exploited to produce better compression performance. Specifically, the proposed scheme partitions subaperture images (SAIs) of an LFI into two groups, namely key SAIs and non-key SAIs. Bi-level view compensation is proposed to exploit the inter-view correlation: first, based on the group of selected key SAIs, learning-based angular super-resolution is performed to compensate non-key SAIs in pixel-wise, during which heterogeneous inter-view correlation between the non-key SAIs is efficiently removed; second, the two groups of SAIs are respectively reorganized as pseudo-sequences, and blockwise motion compensation is carried out with a standard video encoder, during which the homogeneous inter-view correlation is subsequently exploited. The video encoder also helps to remove the intra-view correlation of the SAIs and finally generates the encoded bitstream. Moreover, the bits allocated to each group are optimally determined via model-based rate distortion optimization. Extensive experimental evaluations and comparisons demonstrate the advantage of the proposed framework over existing methods in terms of rate-distortion performance.

Research Area(s)

  • Cameras, compression, Correlation, Decoding, deep learning, disparity, Image coding, Image reconstruction, Image resolution, Light field, rate distortion optimization, Rate-distortion, view compensation