The visual quality of images is outward in image presentation, compression and analysis. Depending on the use, the quality of images may give more information or more experiences to the viewer. However, the relations between mathematical and human methods for grouping the images are not obvious. For example, different humans think differently and so, they make the grouping differently. However, there may be some connections between image mathematical features and human selections. Here we try to find such relations that could give more possibilities for developing the actual quality of images for different purposes. In this study, we present some methods and preliminary results that are based on psychological tests to humans, MPEG-7 based features of the images and face detection methods. We also show some notes and questions belonging to this problem and plans for the future research.
We present techniques for representing spectral images in data communications. The spectral domain of the images is represented by a low-dimensional component image set, which is used to obtain an efficient compression of the high- dimensional spectral data. The component images are compressed using a similar technique as the JPEG- and MPEG- type compressions use to subsample the chrominance channels. The spectral compression is based on Principal Component Analysis (PCA) combined with a color image transmission coding technique of chromatic channel subsampling of the component images. The component images are subsampled using 4:2:2, 4:2:0, and 4:1:1-based compressions. In addition, we extended the test for larger block sizes and larger number of component images than in the original JPEG- and MPEG- standards.
KEYWORDS: Image compression, Principal component analysis, Independent component analysis, Chromium, Data communications, Image transmission, 3D image processing, RGB color model, Signal to noise ratio, Colorimetry
We report a technique for spectral image compression to be used in the field of data communications. The spectral domain of the images is represented by a low-dimensional component image set, which is used to obtain an efficient compression of the high-dimensional spectral data. The component images are compressed using a similar technique as the JPEG- and MPEG-type compressions use to subsample the chrominance channels. The spectral compression is based on Principal Component Analysis (PCA) combined with color image transmission coding technique of 'chromatic channel subsampling' of the component images. The component images are subsampled using 4:2:2, 4:2:0, and 4:1:1-based compressions. In addition, we extended the test for larger block sizes and larger number of component images than in the original JPEG- and MPEG-standards. Totally 50 natural spectral images were used as test material in our experiments. Several error measures of the compression are reported. The same compressions are done using Independent Component Analysis and the results are compared with PCA. These methods give a good compression ratio while keeping visual quality of color still good. Quantitative comparisons between the original and reconstructed spectral images are presented.
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