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

high-frequency components to images, and blocking artifacts introduces high-frequency edges. The distortions caused by JPEG is due to excessive image compression. The human visual sensitivity (HVS) is not sensitive to a change in the low-frequency component of the image. The sensitivity reduces rapidly at low frequency. There is the possibility of losing information while applying a normalization scheme. b) Architecture Model for Non-Screen Content IQA: Here a blind image quality assessment method based on CNN is proposed. The features from the CNN are used for a final quality prediction. The design of the network resembles the design of VGG-16 network. The architecture of CNN for synthetic distortion is shown below in figure 1. The existing dataset consists of a subjective score for each distorted image. The model is fine-tuned to evaluate the subjective scores once the training of neural network is completed with enough training data set. The proposed model is fine-tuned on target subject-specific datasets using a variation of stochastic gradient descent. The kernel size of the convolutions is 3 x 3. A kernel size of two is used in order to diminish the spatial density in both directions by half. The nonlinear activation function ReLU is used. The feature activations of the final convolution layer’s are averaged globally across spatial locations. At the end of the network, three fully connected layers and the ReLU layer are added. Fig. 1: Synthetic CNN Model for Screen Content IQA: Here a model based on neural network for screen content image quality assessment called SCIQA is used. The SCI CNN architecture is shown in figure 2. It consists of 8 convolution layers, 4 max-pooling layers, and 2 fully connected layers. All convolution layers have a filter size of 3 x 3 with stride of 1 pixel. A 2 x 2 pixel kernel with stride of 2 pixels is used in each pooling layer. Each convolutional layer’s boundary is padded with zeros to improve network speed. Fig. 2: SCIQA Model c) Subjective Score After the model has been trained, it is used to predict subjective scores for the distorted image. As illustrated in Fig. 2, the trained network is connected to a global average pooling layer before the fully connected layers. A 128-dimensional feature vector is created by averaging the feature map over the spatial domain. The adaptive moment estimation optimizer (ADAM) was used to change the normal stochastic gradient descent approach for better optimization convergence. VI. E xperiment R esults and A nalysis a) Hardware and Software The experiments has been conducted, and the results were obtained with a laptop with Intel Processor, 8 GB RAM, and 512 GB SDD. As for software, we have used Python as the programming language, and the libraries such as TensorFlow, Keras, SciPy, Matplotlib, etc. in the Jupyter Notebook. The input pipeline for the model is created using TFDS API. b) IQA Dataset The IQA datasets consists of distorted images along withtheir corresponding pristine images. It also have subjective quality scores for distorted images which is obtained after conducting a psychometric experiments using human subjects. Human opinions are taken for these distorted images with reference to pristine images using some pre-defined range for quality measurement. Various IQA datasets were utilized to measure the performance of the proposed algorithm: LIVE IQA dataset, LIVE multiply distorted (LIVE MD) dataset, and UniMiB MD-IVL dataset. The summary of datasets is given in Table I. • The LIVE IQA dataset consists of following types of distortion: WN, JP2K compression, GB, and Rayleigh fast-fading channel distortion [20][21][22]. • The LIVE MD dataset consists of two categories of images based on distortion combinations appplied. First category has images distorted by GB along with JPEG and the second category has images distorted by combination of WN and GB [23]. © 2022 Global Journals Global Journal of Computer Science and Technology Volume XXII Issue I Version I 20 ( )D Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment Year 2022