Abstract
A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.
Highlights
Image processing has found numerous applications in multimedia and smart education [1,2,3], remote sensing and non-destructive control [4,5,6], etc
The goal of this paper is to find such a parameter that controls compression (PCC) value for advanced DCT coder (ADCTC) that guarantees invisibility of introduced distortions and fast compression due to the absence of iterations
To demonstrate this,of we show the plots obtained for integer values of varying in the peak signal-to-noise ratio (PSNR) decreases monotonously if quantization step (QS) increases (Figure 4b)
Summary
Image processing has found numerous applications in multimedia and smart education [1,2,3], remote sensing and non-destructive control [4,5,6], etc. It is widely used in various medical applications [7,8,9]. The most modern imaging systems produce dental images of large size [12,13]. This causes a problem for their storage if the number of daily or monthly acquired images is very large [14], and in image transmission via communication channels in telemedicine [15]
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