Low-Overhead Compression-Aware Channel Filtering for Hyperspectral Image Compression

Both traditional and learning-based hyperspectral image compression methods suffer from significant quality loss at high compression ratios. To address this, we propose a low-overhead, compression-aware channel filtering method. The encoder derives channel filters via Least Squares Regression between lossy compressed and original images. The bitstream, containing the compressed image and filters, is sent to the decoder, where the filters enhance image quality. This simple, compression-aware approach is compatible with any existing framework, enhancing quality while introducing only a negligible increase in bitstream size and decoding time, thereby achieving low overhead. Experimental results show consistent rate-distortion gains, reducing compression rates by 10.51% to 39.81% on the GF-5 dataset with minimal decoding and storage overhead.