Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 22 Issue 1

• The IVL dataset is generated from 10 reference images which is selected from various samples both in terms of low-level features (frequencies, colors) and high level features [24]. This dataset consists of multiple distorted images with 400 images distorted by noise and JPEG distortions. Cardinal rating is provided by human observer for all distorted images corresponding to their reference images in the dataset from a pre-defined scale which is considered as Mean Opinion Score (MOS). Hence, each distorted image in the dataset has a corresponding ground-truth subjective quality score. Table 1: Summary of IQA Datasets Used Dataset References Distortion Total Samples LIVE IQA 29 5 982 LIVE MD 15 2 450 MD-IVL 10 2 400 c) Evaluation Metrics Unlike traditional pixel-based metrics like PSNR, SSIM, etc. which were used in the past for evaluating IQA algorithms, here the evaluation of the IQA algorithm is done using two statistical measures: SROCC and PLCC i.e., Spearman’s rank- order correlation coefficient and Pearson’s linear correlation coefficient respectively. The PLCC is calculated using the following formula: where Sˆi and Si are the predicted and ground-truth subjective scores of the ith image, and µSˆ and µS denote the mean of each. The SROCC is calculated using the following formula: where n denotes the number of images and is the difference between predicted score and ground-truth score of image. d) Results and Analysis i. Performance on Individual Distortion Types There are 5 distortion types in LIVE IQA dataset. The distortion types are Fast Fading (FF), JPEG, Gaussian Blur (GB), JP2K, and White Noise (WN).The PLCC and SROCC values for each individual distortion type is evaluated using the DIQA [25] framework. In Table II the PLCC and SROCC values are compared based on the individual distortion type using DIQA framework. For WN, the PLCC and SROCC values are highest whereas for JPEG, it is the lowest. Since JPEG affects the image less compared to other distortion types, so the highest values are for WN distortion type. Table 2: Comparison of PLCC and SROCC values for different distortion types on LIVE IQA Dataset using DIQA [25] framework Distortion Type PLCC SROCC JPEG 0.9713 0.9551 JP2K 0.9759 0.9686 GB 0.9767 0.9713 WN 0.9881 0.9918 FF 0.9748 0.9622 In Table II, the PLCC and SROCC values are compared based on the individual distortion type using DNSSCIQ frame- work. Table 3: Comparison of PLCC and SROCC values for different distortion types on LIVE IQA Dataset using DNSSCIQ framework Distortion Type PLCC SROCC JPEG 0.9827 0.9624 JP2K 0.9693 0.9656 GB 0.9727 0.9697 WN 0.9881 0.9918 FF 0.9413 0.9447 Figure 3 shows the comparison of SROCC and PLCC values for various distortion types in the LIVE IQA dataset using DNSSCIQ framework. Fig. 3: Comparison of PLCC and SROCC values for various distortion types using DNSSCIQ framework Table 4: Comparison of PLCC and SROCC values for different model depth on LIVE IQA Dataset Model Depth PLCC SROCC 5 0.9699 0.9649 6 0.9769 0.9712 7 0.9799 0.9752 8 0.9809 0.9742 9 0.9767 0.9738 10 0.9792 0.9730 ii. Effect of Model Depth To determine the influence of model depth, six models with different numbers of convolution layers of DIQA [25] was used. Convolution layers 1 to 4 and convolution layer 8 was used for the shortest setting. After the Conv6 layer, two 3 × 3 convolution layers with 64 filters were appended in the longest setting. Figure 4 shows the Global Journal of Computer Science and Technology Volume XXII Issue I Version I 21 ( )D © 2022 Global Journals Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment Year 2022 (1) (2)