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This constant or fixed rate paradigm represents in fact a huge constraint for image compressors. Firstly, it can be hard to obtain with classical entropy coders, because their variable-length codes naturally produce variable bit-rates. Secondly and more importantly, the same compression ratio must be applied to every image, without being able to take into account its content, its degree of interest or even its entropy. Moreover, remote sensing imagery has become a crucial instrument in a large number of civil and military applications and then, image-quality requirements are more and more difficult to satisfy because every final user has specific needs. Thus, as with fixed rate compression some image areas are better compressed than others, image-quality assessments must be established based on worst-case analysis, which provides very low compression ratios, even for state-of-the-art compressors. CNES has been working for the last years in the characterization of image-quality requirements imposed by final users, in order to establish a relationship between the local image characteristics and the associated image quality requirements, or in other words, the tolerated compression losses. As a result, the new functionalities included in the next generation of CNES image compressors will permit to accurately and locally adjust the compression ratio: the target quality level will be adapted for every area in the image taking into account not only its entropy but also its degree of interest. This new trend has required the adoption of variable rate compression, which has had a significant impact in other associated elements such as mission scheduling and storage. Other interesting on-board processing techniques have also been introduced in order to fully exploit the capacities of this new kind of compression. |
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Image compression
Image quality
Computer programming
Satellites
Clouds
Image processing
Remote sensing
