Camera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression

TL;DR

This paper introduces a wavelet-based compression method for raw CFA images that decorrelates coefficients and optimizes camera processing to enhance image quality and compression efficiency.

Contribution

It presents a novel wavelet packet decomposition approach combined with a camera processing pipeline for improved CFA image compression.

Findings

Improved coding efficiency over standard methods

Effective decorrelation of wavelet coefficients in CFA images

Enhanced image quality from compressed raw data

Abstract

We propose a novel lossless and lossy compression scheme for color filter array~(CFA) sampled images based on the wavelet transform of them. Our analysis suggests that the wavelet coefficients of HL and LH subbands are highly correlated. Hence, we decorrelate Mallat wavelet packet decomposition to further sparsify the coefficients. In addition, we develop a camera processing pipeline for compressing CFA sampled images aimed at maximizing the quality of the color images constructed from the compressed CFA sampled images. We validated our theoretical analysis and the performance of the proposed compression scheme using images of natural scenes captured in a raw format. The experimental results verify that our proposed method improves coding efficiency relative to the standard and the state-of-the-art compression schemes CFA sampled images.