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

accuracy comparison among the models on the LIVE IQA dataset. Fig. 4: Comparison of PLCC and SROCC values according to model depth Table III shows the PLCC and SROCC values for different model depth. When the depth was 5, the PLCC and SROCC values were the lowest. When the depth is increased, the correlation coefficient got saturated around 0.97. This may cause overfitting when more convolution layers are used. Hence, it is concluded that the 8 convolutional layers are good enough for the proposed framework. iii. Performance on Individual Datasets The different datasets are used for evaluating the proposed algorithm. The evaluation metrics such as PLCC and SROCC are used. The datasets are having various types of distortions. In some datasets, various distortion types are combine to produce the distorted image. The DIQA method is evaluated on three different IQA dataset individually. The datasets used are LIVE IQA, LIVE MD and MD IVL. Table V shows the comparison of PLCC and SROCC values for individual datasets using DIQA method. For LIVE IQA dataset, the PLCC and SROCC values are highest The DNSSCIQ method is evaluated on three different IQA dataset individually. The datasets used are LIVE IQA, LIVE MD and MD IVL. Table VI shows the comparison of PLCC and SROCC values for individual datasets using DNSSCIQ Table 5: Comparison of PLCC and SROCC values for different IQA Datasets using DIQA framework. Dataset PLCC SROCC LIVE IQA 0.9809 0.9742 LIVE MD 0.9545 0.9561 MD IVL 0.9622 0.9617 Table 6: Comparison of PLCC and SRCC values for different IQA Datasets using DNSSCIQ Dataset PLCC SRCC LIVE IQA 0.9867 0.9799 LIVE MD 0.9656 0.9685 MD IVL 0.9696 0.9702 The PLCC and SROCC values are compared for various IQA datasets like LIVE, LIVE MD and MD IVL in figure 5. Fig. 5: Comparison of PLCC and SROCC values for various IQA datasets using DNSSCIQ framework iv. Reliability Map To find the effect of reliability map, the outputs of various configuration is shown in Table VII. It shows that there is an improvement in performance when reliability map is used. Reliability map helps to create homogeneity across the image irrespective of low- frequency components or high-frequency components in the distorted image. This provides the information about the importance of reliability map. Table 7: Comparison of PLCC and SROCC values with and without Reliability Maps Reliability Map PLCC SROCC w/o 0.9545 0.9561 w 0.9809 0.9742 v. NR-IQA Methods In Table VIII, the PLCC and SROCC metrics of different methods are compared. The different methods are Deep CNN Based Blind Image Quality Predictor (DIQA) [25], Synthetic Convolutional Neural Net- work (S-CNN) and Screen Content Image Quality Assessment Table 8: Comparison of PLCC and SROCC values for different method on LIVE IQA Dataset (SCIQA). The S-CNN is having highest PLCC and SROCC values. Method PLCC SROCC DIQA 0.9809 0.9742 S-CNN 0.9867 0.9799 SCIQA 0.9338 0.9229 Figure 6 shows the reference image on the left and distorted image with gausssian blur on the right. The image is obtained from LIVE IQA dataset. Fig. 6: Reference Image on left and Distorted Image (Gaussian Blur) on right © 2022 Global Journals Global Journal of Computer Science and Technology Volume XXII Issue I Version I 22 ( )D Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment Year 2022