Reduced memory multi-layer multi-component rate allocation for JPEG2000

Remote sensing images are often multispectral in nature and are acquired by on-board sensors in a "push-broom" fashion. These images are compressed and transmitted to ground stations for further analysis. Since they are extremely large, buffering all acquired data before encoding requires huge amounts of memory and introduces latency. Incremental compression schemes work on small chunks of raw data as soon as they are acquired and help reduce buffer memory requirements. However, incremental processing leads to large variations in quality across the reconstructed image. We propose two "leaky bucket" rate control algorithms that can be employed for incrementally compressing hyperspectral images using JPEG2000. Both schemes perform rate control using the fine granularity afforded by JPEG2000. The proposed algorithms have low memory requirements and enable SNR scalability through the use of quality layers. Experiments show that the proposed schemes provide significant reduction in quality variation with no loss in mean overall PSNR performance.