Local Low Rank Approximation With a Parametric Disparity Model for Light Field Compression

低秩近似 参数统计 秩(图论) 仿射变换 像素 数学 光场 维数之咒 算法 计算机科学 人工智能 几何学 统计 组合数学 张量(固有定义)
作者
Elian Dib,Mikaël Le Pendu,Xiaoran Jiang,Christine Guillemot
出处
期刊:IEEE transactions on image processing [Institute of Electrical and Electronics Engineers]
卷期号:29: 9641-9653 被引量:14
标识
DOI:10.1109/tip.2020.3029655
摘要

We address the problem of light field dimensionality reduction for compression. We describe a local low rank approximation method using a parametric disparity model. The local support of the approximation is defined by super-rays. A super-ray can be seen as a set of super-pixels that are coherent across all light field views. A dedicated super-ray construction method is first described that constrains the super-pixels forming a given super-ray to be all of the same shape and size, dealing with occlusions. This constraint is needed so that the super-rays can be used as supports of angular dimensionality reduction based on low rank matrix approximation. The light field low rank assumption depends on how much the views are correlated, i.e., on how well they can be aligned by disparity compensation. We first introduce a parametric model describing the local variations of disparity within each super-ray. We then consider two methods for estimating the model parameters. The first method simply fits the model on an input disparity map. We then introduce a disparity estimation method using a low rank prior. This method alternatively searches for the best parameters of the disparity model and of the low rank approximation. We assess the proposed disparity parametric model, first assuming that the disparity is constant within a super-ray, and second by considering an affine disparity model. We show that using the proposed disparity parametric model and estimation algorithm gives an alignment of super-pixels across views that favours the low rank approximation compared with using disparity estimated with classical computer vision methods. The low rank matrix approximation is computed on the disparity compensated super-rays using a singular value decomposition (SVD). A coding algorithm is then described for the different components of the proposed disparity-compensated low rank approximation. Experimental results show performance gains, with a rate saving going up to 92.61%, compared with the JPEG Pleno anchor, for real light fields captured by a Lytro Illum camera. The rate saving goes up to 37.72% with synthetic light fields. The approach is also shown to outperform an HEVC-based light field compression scheme.
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