System-Aware Compression: Optimizing Imaging Systems from the Compression Standpoint

Speaker:
Yehuda Dar, Ph.D. Thesis Seminar
Date:
Tuesday, 12.6.2018, 10:15
Place:
Taub 337
Advisor:
Prof. Alfred Bruckstein and Prof. Michael Elad

In typical imaging systems, an image/video is first acquired, then compressed for transmission or storage, and eventually presented to human observers using different and often imperfect display devices. While the resulting quality of the perceived output image may severely be affected by the acquisition and display processes, these degradations are usually ignored in the compression stage, leading to an overall sub-optimal system performance. In this work we propose a compression methodology to optimize the system's end-to-end reconstruction error with respect to the compression bit-cost. Using the alternating direction method of multipliers (ADMM) technique, we show that the design of the new globally-optimized compression reduces to a standard compression of a "system adjusted" signal. Essentially, the proposed framework leverages standard compression techniques to address practical settings of the remote source coding problem. We further explain the main ideas of our method using rate-distortion theory for Gaussian signals and linear shift-invariant operators. We experimentally demonstrate our framework for image and video compression using the state-of-the-art HEVC standard, adjusted to several system layouts including acquisition and rendering models. The experiments established our method as the best approach for optimizing the system performance at high bit-rates from the compression standpoint. In addition, we relate the proposed approach also to signal restoration using complexity regularization, where the likelihood of candidate solutions is evaluated based on their compression bit-costs. While complexity-regularized restoration is an established concept, solid practical methods were suggested only for the Gaussian denoising task, leaving more complicated restoration problems without having a general constructive approach. Using our optimization framework we resolve the previously known difficulties and establish a signal restoration approach that leverages standard compression techniques and can address complicated degradation models. We demonstrate our approach by employing the HEVC standard to inpainting and deblurring of images.

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