Pre-demosaic Graph-based Light Field Image Compression
Yung-Hsuan Chao, Haoran Hong, Gene Cheung, and Antonio Ortega

TL;DR
This paper introduces a novel light field image compression method that directly encodes raw sensor data using graph-based transforms, bypassing traditional demosaicking, and achieves superior high-quality compression results.
Contribution
It proposes a new graph lifting transform-based coding scheme that directly encodes raw light field data, improving compression efficiency without pre-processing.
Findings
Outperforms conventional schemes at high PSNRs
Effective exploitation of spatial correlation among sparse pixels
Significant improvement in compression quality for archiving
Abstract
An unfocused plenoptic light field (LF) camera places an array of microlenses in front of an image sensor in order to separately capture different directional rays arriving at an image pixel. Using a conventional Bayer pattern, data captured at each pixel is a single color component (R, G or B).The sensed data then undergoes demosaicking (interpolation of RGB components per pixel) and conversion to an array of sub-aperture images (SAIs). In this paper, we propose a new LF image coding scheme based on graph lifting transform (GLT), where the acquired sensor data are coded in the original captured form without pre-processing. Specifically, we directly map raw sensed color data to the SAIs, resulting in sparsely distributed color pixels on 2D grids, and perform demosaicking at the receiver after decoding. To exploit spatial correlation among the sparse pixels, we propose a novel…
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