Color image quantization is an important operation in the field of color image processing. In this paper, we consider new perceptual image quality metrics for assessment of quantized images. These types of metrics, e.g. DSCSI, MDSIs, MDSIm and HPSI achieve the highest correlation coefficients with MOS during tests on the six publicly available image databases. Research was limited to images distorted by two types of compression: JPG and JPG2K. Statistical analysis of correlation coefficients based on the Friedman test and post-hoc procedures showed that the differences between the four new perceptual metrics are not statistically significant.
The aim of this work was to test the most popular and essential algorithms of the intensity nonuniformity correction of the breast MRI imaging. In this type of MRI imaging, especially in the proximity of the coil, the signal is strong but also can produce some inhomogeneities. Evaluated methods of signal correction were: N3, N3FCM, N4, Nonparametric, and SPM. For testing purposes, a uniform phantom object was used to obtain test images using breast imaging MRI coil. To quantify the results, two measures were used: integral uniformity and standard deviation. For each algorithm minimum, average and maximum values of both evaluation factors have been calculated using the binary mask created for the phantom. In the result, two methods obtained the lowest values in these measures: N3FCM and N4, however, for the second method visually phantom was the most uniform after correction.
Color quantization is an important operation in the field of color image processing. In this paper, we consider a usefulness of the new DSCSI metric to assessment of quantized images. This metric is shown in the background of other useful image quality metrics to evaluate the color image differences and it has also been proven that DSCSI metric achieves the highest correlation coefficients with MOS. For further veriffcation DSCSI metric the combined methods that use to initialize the results of well-known splitting algorithms such as POP, MC, Wu etc. were tested. Experimental results of such combined methods indicate that the Wu+KM approach leading to the best quantized images in the sense of DSCSI metric.
Color image quantization is an often used in such tasks as image compression and image segmentation. In the paper, we continue to consider the usefulness of the new DSCSI metric for evaluating quantized images. Our use of the DSCSI metric confirmed that the color quantization in the CIELAB color space is better than in the basic RGB color space. On several examples we found very good DSCSI suitability in the case of quantization with dithering. During the tests of different dithering algorithms the best results, in terms of DSCSI metric, reached the classical Floyd-Steinberg algorithm at error propagation level of 75-85%.
Proc. SPIE. 9875, Eighth International Conference on Machine Vision (ICMV 2015)
KEYWORDS: Signal to noise ratio, Roentgenium, Image segmentation, Image processing, Gases, Quality measurement, Colorimetry, Image quality, Quantization, Color image processing, Neural networks, RGB color model
Color quantization is still an important auxiliary operation in the processing of color images. The K-means clustering (KM), used to quantize the color, requires an appropriate initialization. In this paper, we propose a combined KM method that use to initialize the results of well-known quantization algorithms such as Wu's, NeuQuant (NQ) and Neural Gas (NG). This approach, assessed by three quality indices: PSNR, ΔE and ΔM, improves the results. Experimental results of such combined quantization indicate that the deterministic Wu+KM and random NG+KM approaches leading to the best quantized images.