Received: 1 July 2020 Revised: 20 November 2020 Accepted: 5 January 2021 IET Image Processing DOI: 10.1049/ipr2.12213 ORIGINAL RESEARCH PAPER A novel image quality assessment method and coefficient of quality for digital solutions of colour blindness Meenakshi S Anshu Singla Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Correspondence Anshu Singla, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India. Email: anshu.singla@chitkara.edu.in Abstract Eyesight is one of the primary senses that human beings have. Reports show that colour blindness, a form of colour vision deficiency (CVD), affects about 8% of the male popula- tion and 0.5% of female population. The Assistive Technology Act of 2004 lays focus on technologies that help individuals with disabilities and deficiencies. With the rapid advance- ment in technologies, several assistive solutions are available for visually impaired or CVD patients. Such solutions involve simulation and compensation of conflicting colours to help the colour blind in the visual perception of colours. Given the increased usage of the web, post the pandemic, these solutions improve the quality of life for the colour blind. Defin- ing the image quality assessment criteria for such digital solutions becomes imperative. The study proposes a novel method for image quality assessment of digital solutions aimed at assisting the colour blind users. The proposed coefficient of quality (CQ) would be use- ful to rank colour compensation and recolouring algorithms. Experiments were conducted with a novel questionnaire set designed for this quality measurement. The results affirm the efficiency of the assessment method proposed. This will also provide objective feedback to the researchers and experts in this area to improve their solutions for CVD patients. 1 INTRODUCTION With the rapid development and rampant use of web for daily routine activities, there has been an increased awareness on mak- ing it accessible to people with vision deficiencies [1]. Colour blindness is one form of colour vision deficiency (CVD) which affects about 8% of the male population and 0.5% of female population [2]. Due to this deficiency, the patients suffering from this anomaly, referred to as colour blind are unable to dis- tinguish colours significantly. In order to assist the colour blind for viewing digital images on the web, a number of colour com- pensation/correction methods have been developed [3]. These colour compensation methods perform colour correction on the conflicting colours thereby improving the perception of them by the colour blind. Efficiency of the colour compensa- tion methods is generally measured through subjective evalua- tion carried out by colour blind and/or normal vision volun- teers. The parameters used for such subjective evaluation is a challenge as human observers (evaluators) are involved. Hence This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology the criteria need to be defined scientifically to rank and mea- sure the efficiency of the colour compensation methods. Such criterion should measure the effectiveness of the method as to how it maintains image quality while recolouring, at the same time improve the perception of the conflicting colours for the colour blind. In this section, the terminology used in the area of CVD, its solutions and parameters for image quality assessment are defined. 1.1 Colour vision deficiency Colour vision deficiency(CVD) refers to the inability to distinguish certain colours from other colours [4–5]. The photoreceptors known as rods and cones in human eyes are responsible for colour vision [6–7]. Rods are responsible for peripheral vision and cones are responsible for colour vision. There are three classes of cones namely L, M and S corre- sponding to the three wavelengths: (i) Long-wavelength (L), IET Image Process. 2021;1–13. wileyonlinelibrary.com/iet-ipr 1