Roumen Kountchev, Roumiana Kountcheva



Color Space Transform for Correlated Images Based on the Recursive Adaptive KLT

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In this work is presented a method for RGB color space transform for a group of correlated images, based on the recursive calculation of the algorithm Adaptive Color KLT (AC-KLT), developed earlier by the authors. However, the use of the algorithm AC-KLT for a group of color images is not sufficiently efficient, because each image should be processed individually. Depending on the image kind (for example, video- or multi-view sequences, medical image sequences, etc.) the pixels of same spatial position can have very high similarity, which in some cases is more than 90%. In such case, there is a high possibility to reduce significantly the information redundancy of the color information for the processed group of images. For this, here is offered the method Recursive AC-KLT, whose basic idea is the AC-KLT to be calculated for the first image in the group only, and for all other images to calculate the values of the difference parameters, needed to restore the color information of the image. The efficiency of the presented approach depends on the mutual correlation between images in the group. The algorithm efficiency is evaluated, and the obtained results confirm its advantage over the basic AC-KLT algorithm. The new method could be used for processing groups of correlated images in the systems for compression and processing of visual information, computer vision, pattern recognition, digital watermarking, etc.



Karhunen-Loève Transform, Adaptive Color Space Transform, Recursive Adaptive Color KLT, Group of Correlated Color Images



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Cite this paper

Roumen Kountchev, Roumiana Kountcheva. (2017) Color Space Transform for Correlated Images Based on the Recursive Adaptive KLT. International Journal of Signal Processing, 2, 72-80


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