Open Access
Research Article
Image quality assessment: Intelligent feature selection using soft computing techniques
*Vikas WassonCorresponding authorvikaswasson.cse@gmail.comDepartment of Computer Science and Engineering I.K.G. Punjab Technical UniversityKapurthala, Punjab, IndiaView full profile → , Birampal Kaurmca.bikrampal@gmail.comDepartment of Computer Science and Engineering I.K.G. Punjab Technical UniversityKapurthala, Punjab, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 04 Sep 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1182
- Pages:
- 1009–1022
Abstract
Image Quality Assessment (IQA), the quantification of a human’s perception of qualityof an image, is a rudimentary problem visual communication system, and beholds unparalleled significance in diverse practical applications like image compression, restoration, and, rendering. The limitations of conventional IQA parameters have motivated the researchers to go for IQA techniques based on the Soft Computing meta-heuristic algorithms that are able to extract features intelligently. In current paper, Whale Optimization Algorithm (WOA) has been applied, which is a meta-heuristic algorithm grounded on swarm behaviour. This technique mimics the demeanor of humpback whales for finding an optimal solution. For the purpose of extensive analysis and testing in Matlab, Colourlab Image Database: Image Quality (CID:IQ) benchmark dataset is applied. Tabular, as well as graphical outcomes, are defended for validating the effectiveness of WOA as compared to traditional methods. WOA understandably surmounts Artificial Neural Networks (ANN) based optimization with regards to Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and other popular IQA parameters. This amelioration is capable of motivating the researchers in the time to come for opting the soft computing mechanisms in several image processing arenas that includes but is not limited to the agricultural sciences, medicine, remote sensing, etc., for assessing the images in pre-processing stages to achieve ameliorated results.
Keywords
Subject Classifications
68T20
References
[1] Zhai, G.; Min, X. (2020): Perceptual Image Quality Assessment: A Survey. Information Sciences, 63, pp. 1-52.[2] Wasson, V.; Kaur, B. (2019): Full Reference Image Quality Assessment from IQA Datasets: A Review. 2019 6th International Conference on Computing for Sustainable Global Development, pp. 735-738.[3] Cui, X.; Shi, Z.; Lin, J.; Huang, L. (2012): The research of image quality assessment methods.Physics Procedia, 25, pp. 485-491.[4] Dass, R.; Yadav, N. (2020): Image Quality Assessment Parameters for Despeckling Filters. Procedia Computer Science, 167, pp. 2382-2392.[5] Kandhway, P.; Bhandari, A. K. (2020): Spatial context-based optimal multilevel energy curve thresholding for image segmentation using soft computing techniques. Neural Computing and Applications, 32, pp. 8901-8937.[6] Basset, M. A.; Fatah, L. A.; Sangaiah, A. K. (2018): Metaheuristic Algorithms: A Comprehensive Review.Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, pp. 185-231.[7] Mirjalili, S. (2019): Genetic Algorithm. Studies in Computational Intelligence, 780, pp. 87-104. [8] Bansal, J.C.(2019): Particle Swarm Optimization. Studies in Computational Intelligence, 779, pp. 11-23. [9] Zhang, T.; Geem, Z. W. (2019): Review of harmony search with respect to algorithm structure. Swarm and Evolutionary Computation, 48, pp. 31-43.[10] Delahaye, D; Chaimatanan, S; Mongeau, M. (2019): Simulated Annealing: From basics to applications. International Series in Operations Research & Management Science, 272, pp. 1-35.[11] Rao, R.V. (2016): Teaching-Learning-Based Optimization Algorithm.Teaching Learning Based Optimization Algorithm, pp. 9-39. [12] Mirjalili, S; Mirjalili, S.M.; Lewis, A. (2014): Grey wolf optimizer. International Journal of Advanced Engineering Software, 69, pp. 46–61.[13] Jiang, J.; Trundle, V.; Ren, J. (2010): Medical image analysis with artificial neural networks. Computerized Medical Imaging and Graphics, 34, pp. 617-631. [14] Mirjalili, S.; Lewis, A. (2016): The whale optimization algorithm.International Journal of Advanced Engineering Software, 95, pp. 51–67.[15] Jiang, T.; Yang, F. (2002): An evolutionary tabu search for cell image segmentation. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 32, pp. 675-678.[16] Hansen, P.; Mladenović, N.; Brimberg, J.; Pérez, J. A. (2019): Variable Neighborhood Search. International Series in Operations Research & Management Science, 272, pp. 57-97. [17] Alamri, H. S.; Alsariera, Y. A.; Zamli, K. Z. (2018): Opposition-based Whale Optimization Algorithm. Advanced Science Letters, 24, pp. 7461–7464.[18] Trivedi, I.N.; Jangir,P.; Jangir, N.; Kumar, A.; Ladumor, D. (2016): Novel adaptive whale optimization algorithm for global optimization. Indian Journal of Science and Technology, 9, pp. 319–326.[19] Ling, Y.; Zhou, Y.; Luo, Q. (2017): Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access, 5, pp. 6168–6186.[20] Kaur, G.; Arora,S. (2018): Chaotic whale optimization algorithm.Journal of Computational Design and Engineering, 5, pp. 275–284.[21] Wu, Z.; Song, F. (2019): Whale optimization algorithm based on improved spiral update position model. Syst. Eng. Theory Pract. 39, pp. 2928–2944.[22] Chen, H.; Yang, C.; Heidari, A. A.; Zhao, X. (2020): An efficient double adaptive random spare reinforced whale optimization algorithm. Expert Systems with Applications, 154, pp. 113018.[23] Huang, H.; Zhang, G.; Chen, S.; Hu, P. (2020): Whale optimization algorithm based on chaos weight and elite guidance.Sens. Microsyst, 39, pp. 113–116.[24] Chu, D.L.; Chen, H.; Wang, X.G. (2019): Whale Optimization Algorithm Based on Adaptive Weight and Simulated Annealing. Acta Electronica Sinica, 47, pp. 992–999.[25] Bozorgi, S. M.; Yazdani, S. (2019): IWOA: An improved whale optimization algorithm for optimization problems. Journal of Computational Design and Engineering, 6, pp. 243–259.[26] Aziz, M. A.; Ewees, A.A.; Hassanien, A.E. (2017): Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Systems with Applications, 83, pp. 242–256.[27] Prakash, D. B.; Lakshminarayana, C. (2017): Optimal siting of capacitors in radial distribution network using whale optimization algorithm. Alexandria Engineering Journal, 56, pp. 499–509.[28] Aljarah, I.; Faris, H.; Mirjalili, S. (2018): Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing, 22, pp. 1–15.[29] Li, Y.; He, Y.; Liu, X.; Guo, X.; Li, Z. (2020): A novel discrete whale optimization algorithm for solving knapsack problems. Applied Intelligence, 50, pp. 3350-3366.[30] Mafarja, M. M.; Mirjalili, S. (2017): Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing, 260, pp. 302–312.[31] Oliva, D.; Aziz, M. A.; Hassanien, A. E. (2017): Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appied. Energy, 200, pp. 141–154.[32] Saidala R. K.; Devarakonda, N. (2018): Improved whale optimization algorithm case study: clinical data of anaemic pregnant woman. Data Engineering and Intelligent Computing, 542, pp. 271-281.[33] Karaboga, D.; Gorkemli, B.; Ozturk, C.; Karaboga, N. (2014): A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42, pp. 21–57.[34] Ong, P. (2014): Adaptive cuckoo search algorithm for unconstrained optimization.The Scientific World Journal, 2014, pp. 1–8.[35] Basset, M. A.; Shahat, D. E.; Henawy, I. E.; Sangaiah, A. K.; Ahmed, S. H. (2018): A novel whale optimization algorithm for cryptanalysis in Merkle-Hellman cryptosystem. Mobile Networks and Applications, 23 pp. 723–733.[36] Nagaraj, S.; Raju, G. S.; Srinadth, (2015): Data encryption and authetication using public key approach. Procedia Computer Science, 48, pp. 126–132.[37] Liu, X.; Pedersen, M.; Hardeberg, J.Y. (2014): CID: IQ – A New Image Quality Database. Image and Signal Processing, ICISP 2014, 8509, pp. 193-202.
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