On holographic transform compression of images

Abstract

Lossy transform compression of images is successful and widespread. The JPEG standard uses the discrete cosine transform on blocks of the image and a bit allocation process that takes advantage of the uneven energy distribution in the transform domain. For most images, 10:1 compression ratios can be achieved with no visible degradations. However, suppose that multiple versions of the compressed image exist in a distributed environment such as the internet, and several of them could be made available upon request. The classical approach would provide no improvement in the image quality if more than one version of the compressed image became available. In this paper, we propose a method, based on multiple description scalar quantization, that yields decompressed image quality that improves with the number of compressed versions available.

Holographic sensing

“…Several follow-up works, e.g., [2,3,4], came up with improved transforms for more efficient holographic encoding processes. Another approach, given in [5], proposed jittered quantization to achieve better precision with multiple instances of compressed image data exploiting differently quantized transform coefficients. A subsampling method for generating holographic data streams that ensure a uniform spatial spread of the sampled locations using low discrepancy sampling patterns can be found in [6].…”