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.