The fast progress of mobile shooting technologies stimulates development of the methods for reliable assessment of image quality. In each case, successful comparison or evaluation requires a proper choice of method. The paper contains a brief tentative reference source for such investigations. We consider the most commonly used subjective methods for assessing and comparing static and video images: ACR – absolute category rating, ACR-HR – absolute category rating with hidden reference, SSCQE – single stimulus continuous quality estimation, DCR – degradation category rating, DSCQR – double stimulus continuous quality rating, PC – pair comparison, PSJ – pairwise similarity judgment, and SDSCE – simultaneous double stimulus for continuous evaluation.
In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard’s DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://github.com/visillect/segmentation.
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