Muhammad MAJID, İmran Fareed NİZAMİ, Khawar KHURSHID

Feature selection algorithm for no-reference image quality assessment using natural scene statistics

Images play an essential part in our daily lives and the performance of various imaging applications isdependent on the user’s quality of experience. No-reference image quality assessment (NR-IQA) has gained importanceto assess the perceived quality, without using any prior information of the nondistorted version of the image. DifferentNR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and thenthese features are used for estimating the image quality score. However, every type of distortion has a different impacton certain sets of features. In this paper, a new feature selection algorithm is proposed for distortion identificationbased image verity and integration evaluation that selects distinct feature groups for each distortion type. The selectionprocedure is based on the contribution of each feature on the Spearman rank order correlation constant (SROCC) score.Only those feature groups are used in the prediction model that have majority features with SROCC score greater thanmean SROCC score of all the features. The proposed feature selection algorithm for NR-IQA shows better performancein comparison to state-of-the-art NR-IQA techniques and other feature selection algorithms when evaluated on threecommonly used databases.

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Krishnan R. Electric Motor Drives: Modeling, Analysis, and Control. 1st ed. Upper Saddle River, NJ, USA: Prentice Hall, 2001.
Soliman HM, Saleem A, Tutunji TA, Al Ratrout S. Robust digital pole-placer for electric drives based on uncertain Diophantine equation and interval mathematics. T I Meas Control 2018; 40: 2546-2559.
Astrom K, Hagglund I. PID Controllers: Theory, Design and Tuning. 2nd ed. Durham, NC, USA: Instrument Society of America, 1995.
Cominos P, Munro N. PID controllers: recent tuning methods and design to specification. IEE Proc Control Theory Appl 2002; 149: 46-53.
Rahimian M, Tavazoei M. Improving integral square error performance with implementable fractional-order PI controllers. Optim Contr Appl Met 2014; 35: 303-323.
Podlubny I. Fractional-order systems and PI λ D µ controllers. IEEE T Automat Contr 1999; 44: 208-213.
Podlubny I, Dorcák L, Kostial I. On fractional derivatives, fractional-order dynamic systems and PI λ D µ controllers. In: 36th IEEE Conference on Decision and Control. 10–12 Dec 1997; San Diego, CA, USA.
Duma R, Dobra P, Trusca M. Embedded application of fractional order control. Electronics Letters 2012; 48: 1526-1528.
Merrikh-Bayat F, Karimi-Ghartemani M. Method for designing PI λ D µ stabilizers for minimum-phase fractionalorder systems. IET Control Theory A 2010; 4: 61-70.
Yeroglu C, Onat C, Tan N. A new tuning method for PI λ D µ controller. In: Proceedings of the Electrical and Electronics Engineering; 5–8 Nov 2009; Bursa, Turkey.
Monje A, Chen YQ, Vinagre BM, Xue D, Feliu V. Fractional-order Systems and Controls: Fundamentals and Applications. 1st ed. London, UK: Springer-Verlag, 2010.
Zeng GQ, Chen J, Dai YX, Li LM, Zheng CW, Chen MR. Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing 2015; 160: 173-184.
Al-Ratrout S, Saleem A, Soliman H. Optimization methods in fractional order control of electric drives: a comparative study. In: 10th IEEE International Symposium on Mechatronics and its Applications (ISMA). 8–10 Dec 2015; Sharjah, UAE.
Ogata K. Modern Control Engineering. 5th ed. Upper Saddle River, NJ, USA: Pearson, 2009.
Tutunji TA, Saleem A. Weighted parametric model identification of induction motors with variable loads using FNN structure and NN2TF algorithm. T I Meas Control 2018, 40: 1645-1658.
Khubalkar S, Chopade A, Junghare A, Aware M, Das S. Design and realization of stand-alone digital fractional order PID controller for buck converter fed DC motor. Circ Syst Signal Pr 2016; 35: 2189-2211.
Luo Y, Zhang T, Lee BJ, Kang C, Chen YQ. Fractional-order proportional derivative controller synthesis and implementation for hard-disk-drive servo system. IEEE T Contr Syst T 2014; 22: 281-289.
Özdemir MT, Öztürk D, Eke İ, Çelik V, Lee KY. Tuning of optimal classical and fractional order PID parameters for automatic generation control based on the bacterial swarm optimization. In: 9th IFAC Symposium on Control of Power and Energy Systems. 9–11 Dec 2015; New Delhi, India.
Al-Alaoui MA. Novel digital integrator and differentiator. Electronics Letters 1993; 29: 376-378.
Al-Alaoui MA. Al-Alaoui operator and the new transformation polynomials for discretization of analogue systems. Electrical Engineering 2008; 29: 455-467.
Chena YQ, Vinagre BM. A new IIR-type digital fractional order differentiator. Signal Processing 2003; 83: 2359- 2365.
Petráš I. An effective numerical method and its utilization to solution of fractional models used in bioengineering applications. Adv Differential Equ 2011; 2011: 1-14.
Isermann R, Münchhof M. Identification of Dynamic Systems: An Introduction with Applications. 1st ed. London, UK: Springer-Verlag, 2010.
Tarhuni NG, Saleem A, Mesbah M. On-line denoising of motor speed control loop using order statistics filtering. In: 10th IEEE International Symposium on Mechatronics and its Applications (ISMA). 8–10 Dec 2015; Sharjah, UAE.
Soliman HM, Bayoumi EHE, Hassan MF. Power system stabilizer design for minimal overshoot and control constraint using swarm optimization. Electr Pow Compo Sys 2009; 37: 111-126.
Çelik V, Demir, Y. Effects on the chaotic system of fractional order PI α controller. Nonlinear Dynam 2010; 59: 143-159.