Hyperspectral images are acquired incrementally in a “push-broom” fashion by on-board sensors. Since these images are highly voluminous, buffering an entire image before compression requires a large buffer and causes 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. The solution to this problem lies in using carefully designed rate control algorithms. We propose two such “leaky bucket” rate control algorithms that can be employed when incrementally compressing hyperspectral images using the JPEG2000 compression engine. They are the Multi-Layer Sliding Window Rate Controller (M-SWRC) and the Multi-Layer Extended Sliding Window Rate Controller (M-EWRC). Both schemes perform rate control using the fine granularity afforded by JPEG2000 bitstreams. The proposed algorithms have low memory requirements since they buffer compressed bitstreams rather than raw image data. Our schemes enable SNR scalability through the use of quality layers in the codestream and produce JPEG2000 compliant multi-layer codestreams at a fraction of the memory used by conventional schemes. Experiments show that the proposed schemes provide significant reduction in quality variation with no loss in mean overall PSNR performance.
JPEG 2000 Part 2 (extensions) contains a number of technologies that are of potential interest in remote sensing applications. These include arbitrary wavelet transforms, techniques to limit boundary artifacts in tiles, multiple component transforms, and trellis-coded quantization (TCQ). We are investigating the addition of these features to the low-memory (scan-based) implementation of JPEG 2000 Part 1. A scan-based implementation of TCQ has been realized and tested, with a very small performance loss as compared with the full image (frame-based) version. A proposed amendment to JPEG 2000 Part 2 will effect the syntax changes required to make scan-based TCQ compatible with the standard.
Many space-borne remote sensing missions are based on scanning sensors that create images a few lines at a time. Moreover, spacecraft typically have limited amounts of available memory, on account of weight, size and power constraints. For these reasons, the JPEG-2000 emerging standard has a requirement for stripe processing in order to meet the needs of the remote sensing profile. This paper first briefly presents the JPEG- 2000 algorithm, highlighting details pertinent to scan-based processing. A technique for meeting the stripe processing requirement is then presented. This technique use a sliding window rate control mechanism that maintains the desired average bit rate over entire images, while retaining a minimum number of bytes in memory at any given time. Results are then presented to show performance over various sliding window sizes.
We present preliminary results from a comparison of image estimation and recovery algorithms developed for use with advanced telescope instrumentation and adaptive optics systems. Our study will quantitatively compare the potential of these techniques to boost the resolution of imagery obtained with undersampled or low-bandwidth adaptive optics; example applications are optical observations with IR- optimized AO, AO observations in server turbulence, and AO observations with dim guidestars. We will compare the algorithms in terms of morphological and relative radiometric accuracy as well as computational efficiency. Here, we present qualitative comments on image results for two levels each of seeing, object brightness, and AO compensation/wavefront sensing.
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