Camera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression
This addresses compression for raw camera data, which is incremental as it builds on existing wavelet methods with specific optimizations.
The paper tackles compression of raw sensor data from color filter arrays by proposing a wavelet-based scheme that decorrelates coefficients and optimizes camera processing. The method improves coding efficiency compared to standard and state-of-the-art approaches for CFA sampled images.
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.