Lossy Compression of Remote Sensing Images with Controllable Distortions

Abstract

In this chapter, approaches to provide a desired quality of remote sensing images compressed in a lossy manner are considered. It is shown that, under certain conditions, this can be done automatically and quickly using prediction of coder performance parameters. The main parameters (metrics) are mean square error (MSE) or peak signal-to-noise ratio (PSNR) of introduced losses (distortions) although prediction of other important metrics is also possible. Having such a prediction, it becomes possible to set a quantization step of a coder in a proper manner to provide distortions of a desired level or less without compression/decompression iterations for single-channel image. It is shown that this approach can be also exploited in three-dimensional (3D) compression of multichannel images to produce a larger compression ratio (CR) for the same or less introduced distortions as for component-wise compression of multichannel data. The proposed methods are verified for test and real life images.

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

“…As an example, for the case of QS = 20, PSNR and PSNR-HVS-M for the test image Frisco are practically the same (about 40 dB). For the test images Airfield and Diego that have more complex structures, PSNR-HVS-M values are also about 40 Note that distortions' invisibility happens if QS is smaller than 18…20 for grayscale images represented as 8-bit data arrays [23] (more generally, if QS ≤ DR / (12…13)). One should also keep in mind that the use of the same QS leads to sufficiently different CR.…”

“…Here we would like to recall some already known aspects and dependencies that will be exploited below. First, for the same CR, considerably larger distortions can be introduced into images with higher complexity [40]. Moreover, larger distortions can be introduced into more complex and/or noisier images for the same PCC, for example, quantization step (QS) of coders based on orthogonal transforms (discrete cosine transform (DCT) or wavelets [40]).…”

“…First, for the same CR, considerably larger distortions can be introduced into images with higher complexity [40]. Moreover, larger distortions can be introduced into more complex and/or noisier images for the same PCC, for example, quantization step (QS) of coders based on orthogonal transforms (discrete cosine transform (DCT) or wavelets [40]). Second, the desired quality of a compressed image (a value of a metric that characterizes image quality) can be nowadays predicted and provided with an appropriate accuracy [40,41], at least, for some coders based on DCT as, e.g., the coder AGU [42].…”

“…Moreover, larger distortions can be introduced into more complex and/or noisier images for the same PCC, for example, quantization step (QS) of coders based on orthogonal transforms (discrete cosine transform (DCT) or wavelets [40]). Second, the desired quality of a compressed image (a value of a metric that characterizes image quality) can be nowadays predicted and provided with an appropriate accuracy [40,41], at least, for some coders based on DCT as, e.g., the coder AGU [42]. Third, there exist certain dependencies between values of conventional (e.g., PSNR) and/or visual quality metrics and accuracy of correct classification of an entire image or classes [43].…”