Abstract
This work considers the problem of quality assessment of multichannel image visualization methods. One approach to such an assessment, the Escore quality measure, is studied. This measure, initially proposed for decolorization methods evaluation, can be generalized for the assessment of hyperspectral image visualization methods. It is shown that Escore does not account for the loss of local contrast at the supra-pixel scale. The sensitivity to the latter in humans depends on the observation conditions, so we propose a modified wEscore measure which includes the parameters allowing for the adjustment of the local contrast scale based on the angular resolution of the images. We also describe the adjustment of wEscore parameters for the evaluation of known decolorization algorithms applied to the images from the COLOR250 and the Cadik datasets with given observational conditions. When ranking the results of these algorithms and comparing it to the ranking based on human perception, wEscore turned out to be more accurate than Escore.
Funder
Russian Science Foundation
Publisher
Samara National Research University
Subject
Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics
Reference39 articles.
1. Zhizhin MN, Elwidge K, Poyda AA, Godunov AI, Velikhov VE, Erokhin GN, Alsynbaev KS, Bryksin VM. Using remote sensing data to monitor hydrocarbon production [In Russian]. Informatsionnye Tekhnologii i Vychslitel'nye Sistemy 2014; 3: 97-111.
2. ENVI – Image processing and analysis solution. Source: .
3. ERDAS Imagine. Source: .
4. Sarycheva A, Grigoryev A, Sidorchuk D, Vladimirov G, Khaitovich P, Efimova O, Gavrilenko O, Stekolshchikova E, Nikolaev E, Kostyukevich Y. Structure-preserving and perceptually consistent approach for visualization of mass spectrometry imaging datasets. Anal Chem 2020; 93(3): 1677-1685. DOI: 10.1021/acs.analchem.0c04256.
5. Smets T, Verbeeck N, Claesen M, Asperger A, Griffioen G, Tousseyn T, Waelput W, Waelkens E, De Moor B. Evaluation of distance metrics and spatial autocorrelation in uniform manifold approximation and projection applied to mass spectrometry imaging data. Anal Chem 2019; 91(9): 5706-5714. DOI: 10.1021/acs.analchem.8b05827.