A Fast Method of Visually Lossless Compression of Dental Images
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Data Compression Techniques · Image and Signal Denoising Methods · Image and Video Quality Assessment
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information and Communication Technologies, National Aerospace University, Chkalova Str. 17, 61070 Kharkiv, Ukraine
Department of Therapeutic Dentistry & Department of Pediatric Dentistry and Implantology, Kharkiv National Medical University, 4 Nauky Avenue, 61022 Kharkiv, Ukraine
Computational Imaging Group, Tampere University, 33720 Tampere, Finland
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