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Correlation estimation from compressed images

机译：根据压缩图像进行相关估计

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摘要

This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where the images are represented under the form of linear measurements due to low complexity sensing or security requirements. We assume that the images are correlated through the displacement of visual objects due to motion or viewpoint change and the correlation is effectively represented by optical flow or motion field models. The correlation is estimated in the compressed domain by jointly processing the linear measurements. We first show that the correlated images can be efficiently related using a linear operator. Using this linear relationship we then describe the dependencies between images in the compressed domain. We further cast a regularized optimization problem where the correlation is estimated in order to satisfy both data consistency and motion smoothness objectives with a Graph Cut algorithm. We analyze in detail the correlation estimation performance and quantify the penalty due to image compression. Extensive experiments in stereo and video imaging applications show that our novel solution stays competitive with methods that implement complex image reconstruction steps prior to correlation estimation. We finally use the estimated correlation in a novel joint image reconstruction scheme that is based on an optimization problem with sparsity priors on the reconstructed images. Additional experiments show that our correlation estimation algorithm leads to an effective reconstruction of pairs of images in distributed image coding schemes that outperform independent reconstruction algorithms by 2-4 dB.
机译：本文解决了压缩图像集合中的相关估计问题。由于低复杂度感测或安全要求，我们考虑一个框架，其中图像以线性测量的形式表示。我们假设图像是由于运动或视点变化而通过视觉对象的位移而关联的，并且该关联可以通过光流或运动场模型有效地表示。通过联合处理线性测量值，可以在压缩域中估计相关性。我们首先显示可以使用线性算子有效地关联图像。然后，使用这种线性关系来描述压缩域中图像之间的依赖性。我们进一步投射了一个正则化优化问题，其中使用Graph Cut算法估算相关性，以满足数据一致性和运动平滑度目标。我们详细分析了相关估计性能并量化了由于图像压缩带来的损失。在立体声和视频成像应用中的大量实验表明，我们的新颖解决方案与在相关估计之前实施复杂图像重建步骤的方法保持竞争优势。最后，我们在一种新颖的联合图像重建方案中使用了估计的相关性，该方案基于对重建图像具有稀疏先验的优化问题。其他实验表明，我们的相关估计算法可在分布式图像编码方案中有效地重建图像对，其效果比独立的重建算法要好2-4 dB。

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