###### 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) code 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. image lossy compression; optimal operation point; quality prediction; noise; discrete cosine transform; automation Article  
BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters  
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of providing an appropriate trade-off between the introduced distortions and the reached CR arises [13; 14]. The priority of requirements depends upon the application at hand and it can be necessary or desired to provide a given CR (or a CR not less than a given threshold). Meanwhile, it can also be necessary (or desired) to provide a desired quality (or a quality not worse than desired), but there can also be other requirements or restrictions that might concern the necessity for relying on standards or to reach a trade-off quickly, to save resources and to carry out a compression fully automatically, etc. In this paper, we focus on providing high quality with the desire to have quite a high CR and to perform the compression quite quickly and automatically. If the quality degradation due to a lossy compression is limited, it is possible to expect that the RS data classification or object (e.g., crack) detection are performed well enough, and that the RS data being visualized are of a proper quality for analysis and so on [15; 16; 17]. In many cases, it is supposed that the images to be compressed are noise-free or, at least, that the noise is invisible. This is often valid in practice but, meanwhile, there are quite a number of practical situations when noise is visible and its presence cannot be neglected; for example, noise (speckle) is always seen in radar images [18; 19], some component images of hyperspectral and multispectral RS data are noisy [20], the signal-to-noise ratio can be low in night-light images [21] or in images acquired in bad illumination conditions and so on. The influence of noise first attracted the attention of researchers more than 20 years ago [22; 23]. It has been demonstrated that the lossy compression of noisy images has two main peculiarities. First, a specific noise filtering effect is observed.