几何相关图像的压缩线性度量分布式表示
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
摘要：
本文讨论的问题，分布式编码的图像的相关性是由物体的运动或定位的视觉传感器。它集中在图像编码与压缩线性测量的问题。我们提出了一个基于几何的相关性模型，以描述图像对中的共同信息。我们假设自然图像的组成成分可以通过经历局部变换的视觉特征（例如，翻译）在不同的图像。我们首先确定突出的视觉特征，通过计算一个稀疏近似的参考图像与字典的几何基函数。然后，我们提出了一个正则化的优化问题，以估计相应的功能，在相关的图像量化的线性测量。估计的特征必须符合压缩信息，并表示图像之间的一致变换。相关模型由对应特征之间的相对几何变换给出。然后，我们提出了一个有效的联合解码算法，估计压缩图像，使他们保持一致的量化测量和相关模型。实验结果表明，该算法能有效地估计多视图数据集中图像间的相关性。此外，所提出的算法提供了有效的解码性能，有利地比较独立的编码解决方案，以及国家的最先进的分布式编码方案的基础上视差学习。
英文摘要：
This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or positioning of the vision sensors. It concentrates on the problem where images are encoded with compressed linear measurements. We propose a geometry-based correlation model in order to describe the common information in pairs of images. We assume that the constitutive components of natural images can be captured by visual features that undergo local transformations (e.g., translation) in different images. We first identify prominent visual features by computing a sparse approximation of a reference image with a dictionary of geometric basis functions. We then pose a regularized optimization problem to estimate the corresponding features in correlated images given by quantized linear measurements. The estimated features have to comply with the compressed information and to represent consistent transformation between images. The correlation model is given by the relative geometric transformations between corresponding features. We then propose an efficient joint decoding algorithm that estimates the compressed images such that they stay consistent with both the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in multi-view datasets. In addition, the proposed algorithm provides effective decoding performance that compares advantageously to independent coding solutions as well as state-of-the-art distributed coding schemes based on disparity learning.
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