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

Bose et al. [17] proposed a neural network based method for IQA that enables feature learning and regression in an end-to-end framework. A siamese network using CNN is used with both original and distorted images as input for FR-IQA whereas one branch of siamese network is discarded where the distorted image is used as input for NR-IQA. It incorporates a weighted average patch aggregation that implements a method for pooling local patch qualities to global image quality. Based on selected feature similarity and ensemble learning, Hammou et al. [18] suggested an ensemble of gradient boosting (EGB) measure. To characterise the perceptual quality distance between the pristine and distorted/processed images, the features obtained from various layers of deep CNN are analyzed. Kang et al. [19] proposed a compact CNN for calculating image quality and identifying distortions. The parameter reduction at the fully connected layers makes this model less prone to overfitting. III. M otivation The main motivation behind image quality assessment is to quantify visual perception of humans for image quality so that quality evaluation of images can be done. Digital images intend to degrade during the process from generation to consumption. Different kind of distortions are introduced in the process of transmission, post processing, or compression of images such as white noise, Gaussian blur, or impeding artifacts. This affects the visual experience of users while seeing image content on various online websites. A depend- able IQA algorithm can assist in quantifying the quality of images acquired from the web and also helps to measure the performance of image processing algorithms precisely, such as image-compression and super-resolution, from the point view of a human. a) Drawbacks of Using CNNs to NR-IQA Because of its high representation capability and improved performance, convolutional neural networks are the most popular type of neural networks for working with image data. The quantity of the training dataset has a major impact on the performance of neural networks. However, compared to the most frequent computer vision dataset, the currently available IQA datasets are substantially smaller. In contrast to classification datasets, IQA datasets necessitate a time- consuming and sophisticated psychometric experiment. Various data augmentation techniques, such as horizontal reflection, rotation, and cropping, can be employed to enhance the size of the training dataset. The human visual system’s (HVS) perception process is made up of several complex processes. It makes training a deep learning model more difficult with a limited dataset. The visual sensitivity of the HVS changes with the spatial frequency of stimuli, and texture prevents concurrent picture alterations. b) Applications of IQA IQA has a diverse variety of computer vision and image processing usage. For example: • For quantization, an image compression algorithm can use quality as an optimization parameter. • Image transmission systems can be created to assess quality and distribute different streaming resources accordingly. • Image recommendation algorithms can be created to rank photos according to perceptual image quality. • Depending on the image quality desired, several device characteristics for digital cameras can be modified. IV. P roblem S tatement Image Quality Assessment is different from other image processing applications. Unlike segmen- tation, object detection or classification, preparing IQA dataset is time-consuming and requires complicated psychometric experiments. Therefore, the generation of huge datasets is costly because it requires the supervision of experts which are responsible of ensuring the correct implementation of the experiments. The next drawback is that data augmentation is not preferred because the pixel structure of original images must not be changed. In this paper, an image quality assessment model is developed to calculate the quality of blind images. The distorted images and their ground-truth subjective scores are used for training the CNN model. V. M ethodology a) Image Normalization Image normalization is required because it ensures that the data distribution of each input pixel in the image is consistent. This aids in convergence while doing the training of the neural network. The mean is subtracted from each pixel value, and the result is divided by the standard deviation. Such data would be distributed in a Gaussian distribution centered at zero. The pixel numbers for image input must be positive. As a result, the normalized data must be scaled in the range [0,1] or [0,255]. First, preprocessing is done where the input images are transformed into grayscale, and then they are reduced from their low-pass filtered images. The low-frequency image is retrieved by downscaling the input image to 1/4 and upscaling it again to the original image size. A Gaussian low-pass filter along with subsampling was used to resize the images. The reasons for this kind of normalization is that image distortion doesn’t affect the low-frequency component in images. For instance, GB removes high- frequency details, white noise (WN) introduces random Global Journal of Computer Science and Technology Volume XXII Issue I Version I 19 ( )D © 2022 Global Journals Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment Year 2022