BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters

Abstract

With a resolution improvement, the size of modern remote sensing images increases. This makes it desirable to compress them, mostly by using lossy compression techniques. Often the images to be compressed (or some component images of multichannel remote sensing data) are noisy. The lossy compression of such images has several peculiarities dealing with specific noise filtering effects and evaluation of the compression technique’s performance. In particular, an optimal operation point (OOP) may exist where quality of a compressed image is closer to the corresponding noise-free (true) image than the uncompressed (original, noisy) image quality, according to certain criterion (metrics). In such a case, it is reasonable to automatically compress an image under interest in the OOP neighborhood, but without having the true image at disposal in practice, it is impossible to accurately determine if the OOP does exist. Here we show that, by a simple and fast preliminary analysis and pre-training, it is possible to predict the OOPs existence and the metric values in it with appropriate accuracy. The study is carried out for a better portable graphics (BPG) coder for additive white Gaussian noise, focusing mainly on one-component (grayscale) images. The results allow for concluding that prediction is possible for an improvement (reduction) in the quality metrics of PSNR and PSNR-HVS-M. In turn, this allows for decision-making about the existence or absence of an OOP. If an OOP is absent, a more “careful” compression is recommended. Having such rules, it then becomes possible to carry out the compression automatically. Additionally, possible modifications for the cases of signal-dependent noise and the joint compression of three-component images are considered and the possible existence of an OOP for these cases is demonstrated.

BPG-Based Lossy Compression of Three-Channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters

“…In addition, OOP compression can be beneficial for image classification [39]. On the other hand, if OOP does not exist, a more "conservative" compression is desired [40].…”

“…The authors of [44,45] proved that the OOP can be reached automatically for DCT-based coders for signal-dependent noise with a priori known characteristics. The case of BPG coder applied to noisy images has been considered in [40,46]. The case of single-component images has been studied in depth there.…”

“…The case of single-component images has been studied in depth there. However, for three-channel images, only preliminary analysis results showing that the OOP can exist were given [40]. Note that the results in [47] show that the OOP is more likely if multichannel noisy images are jointly compressed compared with component-wise compression.…”

“…Note that the results in [47] show that the OOP is more likely if multichannel noisy images are jointly compressed compared with component-wise compression. The possibility of predicting the existence of the OOP has been demonstrated in [36,40].…”

BPG-based compression analysis of Poisson-noisy medical images

“…This effect was first observed in [20][21][22], and it occurs for compression methods that use various orthogonal transforms [17][18][19][20]. One important task is to choose encoder parameters such that compression is performed near the optimal operating point (OOP) [20,[23][24][25][26], which ensures that the decoded image is as close as possible to the original image based on the chosen criterion. The existence of the optimal operating point (OOP) has been demonstrated for various types of noise [20], compression methods based on discrete cosine transform (DCT) [27,28], and wavelets [22].…”