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

Mittal et al. [2] proposed a NSS-based distortion-generic IQA model. This model works best in the spatial domain. BRISQUE does not calculate the distortion-specific features, such as blur, blocking, or ringing. Rather, it uses scene statistics of locally normalized luminance coefficients to quantify losses of naturalness in the image. Li et al. [3] trained a general regression neural network (GRNN) to assess the quality of image, relative to the human subjective opinion, across a diverse range of distortion types. The features used for assessing the quality of the image include gradient of the distorted image, entropy of phase congruency image, mean value of the phase congruency image, and entropy of the distorted image. Moorthy and Bovik [4] introduced DIIVINE (Distortion Identification-based Image Verity and INte grity Evaluation). This algorithm evaluates the quality of a distorted image without the original images. It is a 2- stage based technique where image distortion identify- cation is done first and then image quality assessment is done based on distortion type. Tang et al. [5] presented a framework, where potentially neither the degradation process nor the ground truth image is known. The method is based on a set of low-level image features. The image quality characteristics are derived from original image measurement and texture statistics. Here, a machine learning technique is used to learn a mapping from these features to the subjective quality scores. Doermann et al. [6] obtained the basic feature set by the extraction of local features. Then, using the features from the CSIQ database, by adopting K-means clustering, the codebooks with 100 centers was retained. In the mean time, the method proposes high order features: variance, mean, and skewness. The input features are used to get distances to K clusters. Then the method performs regression over three distances. It is sensitive to diverse distortion types. Fang et al. [7] proposed a quality assessment methodology based on statistical structural and luminance features (NRSL). The evaluations were done on 4 synthetically and 3 naturally distorted image datasets. In terms of high correlation with human subjective judgments, the employed NRSL metric compares favorably to relevant BIQA models. Support vector regression was used to establish the complex nonlinear relationship between feature space and quality score. It was unable to use NRSL for various distortions in chromatic component of the image. Kim and Lee [8] proposed Deep Image Quality Assessment (DeepQA) where the behavior of HVS is analyzed from the data distribution of IQA datasets. The sensitivity maps were evaluated for various distortion types and degrees of distortion. Subjective score requires reference images. Y. Li et al. [9] proposed SESANIA where shearlet transform and deep neural networks (stacked autoencoders) is used instead of conventional regression machines. This framework is enhanced to calculate the quality of image in local regions. Liu, Weijer, and Bagdanov [10] used Siamese Network for ranking images in order of image quality. The relative image quality is known for which synthetically generated distortions are used. This helps to solve the issue of the limited size of the IQA dataset. These ranking image sets can be constructed automatically without the requirement of painful effort of labeling by human. This technique uses synthetic images. Saad et al. [11] introduced a Natural Scene Statistics (NSS) based methodology which uses discrete cosine transform (DCT) technique. This method was based on a Bayesian technique to evaluate the image quality scores when features retrieved from the image is given. Kede Ma et al. [12] proposed an optimized neural network for assessing blind image quality. First, distortion is identified and then the quality prediction is done using the features obtained during distortion identification. Fei Gao et al. [13] proposed Deep Similarity for image quality assessment (Deep Sim) framework. First, the features of the original and tested images are received from Image Net pretrained VGGNet without any further training. Then, the local similarities between the features of those corresponding images are calculated. At last, the local quality indices are eventually pooled altogether to evaluate the quality index. Min et al. [14] proposed the concept of multiple pseudo reference images, which are generated from distorted images by applying various levels of distortion. As a result, the quality of a pseudo reference image (PRI) is generally lower than that of its distorted counterpart. The idea behind this methodology is to generate a series of PRI by further degrading the distorted image, and then use local binary patterns (LBP) to calculate the similarity between them to evaluate its quality. Talebi and Milanfar [15] proposed a convolutional neural network based methodology known as NIMA which is used to predict the distribution of human opinion scores. The network may be used to score images in a way that closely resembles human perception. Its goal is to forecast image technical and aesthetic attributes. Hou et al. [16] proposed a blind IQA that directly learns qualitative evaluation and predicts scalar values for general usage and fair comparison. Here, the natural scene statistics features are used to represent the images. A discriminative model is trained to distinguish the characteristics into five ranks, that correlate with five rational notion, i.e., bad, poor, fair, good and excellent. © 2022 Global Journals Global Journal of Computer Science and Technology Volume XXII Issue I Version I 18 ( )D Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment Year 2022