Lossless White Balance For Improved Lossless CFA Image and Video Compression

TL;DR

This paper introduces a lossless white balance method that preprocesses raw sensor data, reducing chrominance bandwidth and enhancing the efficiency of lossless CFA image and video compression.

Contribution

A novel lifting-based lossless white balance algorithm that improves compression efficiency of raw sensor data by reducing chrominance bandwidth before encoding.

Findings

Reduces chrominance signal bandwidth in raw data

Improves lossless compression efficiency for CFA images and videos

Enhances existing coding schemes with pre-processing step

Abstract

Color filter array is spatial multiplexing of pixel-sized filters placed over pixel detectors in camera sensors. The state-of-the-art lossless coding techniques of raw sensor data captured by such sensors leverage spatial or cross-color correlation using lifting schemes. In this paper, we propose a lifting-based lossless white balance algorithm. When applied to the raw sensor data, the spatial bandwidth of the implied chrominance signals decreases. We propose to use this white balance as a pre-processing step to lossless CFA subsampled image/video compression, improving the overall coding efficiency of the raw sensor data.