Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements

TL;DR

This paper introduces a geometry-based correlation model and an efficient joint decoding algorithm for distributed image coding with compressed linear measurements, improving correlation estimation and decoding performance in multi-view datasets.

Contribution

It proposes a novel geometry-based correlation model and a joint decoding algorithm for distributed image coding using compressed measurements, enhancing correlation estimation and decoding accuracy.

Findings

Effective correlation estimation in multi-view datasets.

Decoding performance surpasses independent coding and disparity learning schemes.

Algorithm efficiently estimates images consistent with measurements and geometric correlation.

Abstract

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.