This paper concerns lossy compression of images corrupted by additive noise. The main contribution of the paper is that analysis is carried out from the viewpoint of compressed image visual quality. Several coders for which the compression ratio is controlled in different manner are considered. Visual quality metrics that are the most adequate for the considered application (WSNR, MSSIM, PSNR-HVS-M, and PSNR-HVS) are used. It is demonstrated that under certain conditions visual quality of compressed images can be slightly better than quality of original noisy images due to image filtering through lossy compression. The

It is shown that images can be subject to lossy compression in such a way that introduced distortions are not visible. For this purpose, two modern visual quality metrics, MSSIM and PSNR-HVS-M, can be used. Their values are to be provided not less than 0.99 and 40 dB, respectively, and the corresponding lossy compression is to be carried out. Attained compression ratio (CR) depends upon image properties and a coder used. The proposed methodology of lossy compression can be successfully exploited in remote sensing and medical imaging with producing CR by several times larger than the best lossless image compression techniques.

Image information technology has become an important perception technology considering the task of providing lossy image compression with the desired quality using certain encoders Recent researches have shown that the use of a two-step method can perform the compression in a very simple manner and with reduced compression time under the premise of providing a desired visual quality accuracy. However, different encoders have different compression algorithms. These issues involve providing the accuracy of the desired quality. This paper considers the application of the two-step method in an encoder based on a discrete wavelet transform (DWT). In the experiment, bits per pixel (BPP) is used as the control parameter to vary and predict the compressed image quality, and three visual quality evaluation metrics (PSNR, PSNR-HVS, PSNR-HVS-M) are analyzed. In special cases, the two-step method is allowed to be modified. This modification relates to the cases when images subject to lossy compression are either too simple or too complex and linear approximation of dependences is no more valid. Experimental data prove that, compared with the single-step method, after performing the two-step compression method, the mean square error of differences between desired and provided values drops by an order of magnitude. For PSNR-HVS-M, the error of the two-step method does not exceed 3.6 dB. The experiment has been conducted for Set Partitioning in Hierarchical Trees (SPIHT), a typical image encoder based on DWT, but it can be expected that the proposed method applies to other DWT-based image compression techniques. The results show that the application range of the two-step lossy compression method has been expanded. It is not only suitable for encoders based on discrete cosine transform (DCT) but also works well for DWT-based encoders.

Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given.

The task of prediction practical efficiency of filtering on the basis of the discrete cosine transform (DCT) methods is considered. It is shown that it is possible to estimate the MSE values of images to be processed by means of calculation rather simple statistics of DCT coefficients. Moreover, the quasi-optimal value of threshold parameter for DCT filtering methods can be easy evaluated as well. The results are presented for different additive Gaussian noise levels and a set of gray-scale test images. Developed method provides an opportunity to decide is it worth applying filtering or not.