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.
The procedure of colorimetric calibration of the camera ensures accurate and repeatable acquisition of the scene colors. The most common approach defines the calibration only as the color transformation between the camera image colors and colorimetric colors. The only condition for image acquisition is an uniform illumination of the scene. Unfortunately, such assumption does not include many distortions caused by image acquisition. One of them is a vignetting, which can be described as a decrease of the light intensity from the image center to the image corners. This phenomenon causes the same effects as non-uniform illumination, which is the change of color values of the same object depending on image coordinates. This paper is an attempt to analyze the influence of vignetting correction on the results of the camera colorimetric calibration. The conducted experiment in uniform light conditions shows that the improvement of calibration quality depends on the chosen vignetting correction method.
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%.
The vignetting refers to the fall-off of pixel intensity from the center towards the edges of the image. The correction of vignetting is a required pre-processing step in many applications of machine vision. In this paper, we propose a new local polynomial model of vignetting. The order of the polynomial is a parameter of the model and allows to fit the model to the real vignetting of the camera-lens system. The novelty of the proposed model is a usage of local fitting of the model to vignetting data, in contrast to the global models described in the literature. The new model was tested on two camera-lens systems with radial and non-radial vignetting, and has been compared with methods known from the literature. Based on the obtained results the proposed model gives the best quality of vignetting correction among the tested models of vignetting.
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.
Image segmentation is one of the most difficult steps in the computer vision process. Pixel clustering is only one among many techniques used in image segmentation. In this paper is proposed a new segmentation technique, making clustering in the five-dimensional feature space built from three color components and two spatial coordinates. The advantages of taking into account the information about the image structure in pixel clustering are shown. The proposed 5Dk-means technique requires, similarly to other segmentation techniques, an additional postprocessing to eliminate oversegmentation. Our approach is evaluated on different simple and complex images.
In this paper we show different methods of defining and computing of colofulness of the image from digital image
processing point of view. All experiments have been carried out on the set of natural color images with different
perceptual colorfulness. We have tested the images using following simple colorfulness estimate based on statistical
parameters of the pixel cloud along red-green and yellow-blue axes. During image processing the colorfulness of the
image can be changed by increasing after color enhancement or by decreasing after image compression. Sometimes the
colorfulness of the image should be invariant. We have presented it on examples, which show that the colorfulness can
be useful for evaluating the color quantization algorithms beside such traditional performance functions as RMSE and
Typical single-chip CCD co0lor camera provides output signal containing RGB components of acquired image. Strong inter- channel correlation of that signal, unacceptable for some applications, is shown. Therefore, some kind of RGB color space transform is required to decrease this correlation. Only the Karhunen-Loeve transform (KLT) secures compete decorrelation. However, possible applications of this method are limited by the fact that coefficients of transform matrix must be calculated for each image separately. In the paper details of effective implementation such transform of color images are described. The examples of KLT applications in the field of color image processing are quoted. Using the standard color images, principal properties of KLT are shown, i.e. a complete decorrelation of color components and energy compaction in first components. This paper present also an approach that allows determining KLT coefficients that are constant for certain class of images and guarantee complete decorrelation simultaneously. A methodology for finding these coefficients is briefly described and some remarks on criteria of image selection for this class are made.
Object counting in the scene can be used for visual inspection processes. Highlights are the characteristic bright spots occurring on the surfaces of individual objects. In this work the importance of highlights in image processing is described and some reflection models are briefly reviewed. First of all the possibilities of the Dichromatic Reflection Model (DRM) are presented. The paper presents a new idea of object counting based on highlights counting on surfaces of objects. Object counting is composed of following stages: extraction of highlights in color image by thresholding of selected IHS components, morphological consolidation of extracted highlight regions and region counting (labeling) in binary image. Object counting takes into consideration the number of used light sources, because in case of more than one lighting source multiple highlights per object are observed. This is realized without using reflection model. The proposed method was tested for a number of different, real world images. Input images were acquired directly from 1-CCD color camera without preprocessing. Best results were achieved for optically inhomogeneous (e.g. plastic) chromatic objects in dark background. Typical lighting system based on two fluorescent tubes (5400 K) was used. The method seems promising for practical applications.