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

Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment Dilip Chaudhary α & Venkatesh σ Abstract - In the current world, user experience in various platforms matters a lot for different organizations. But providing a better experience can be challenging if the multimedia content on online platforms is having different kinds of distortions which impact the overall experience of the user. There can be various reasons behind distortions such as compression or minimal lighting condition while taking photos. In this work, a deep CNN-based Non-Screen Content and Screen Content NR-IQA framework is proposed which solves this issue in a more effective way. The framework is known as DNSSCIQ. Two different architectures are proposed based upon the input image type whether the input is a screen content or non-screen content image. This work attempts to solve this by evaluating the quality of such images. The proposed model can be used for both screen content as well as non-screen content image to calculate the image quality score. The performance of the proposed framework was calculated using different datasets like LIVE IQA, LIVE MUL and IVL and it is found that the model is giving state-of- the-art results for all these datasets. Experiments were conducted to find the performance results for various distortion types in LIVE IQA dataset. Index Terms deep learning, convolutional neural network (CNN), screen content image (SCI), image quality assessment (IQA), no-reference IQA (NR-IQA). I. I ntroduction mage quality assessment is a subject of extensive analysis over the last four decades. Different multimedia applications streaming images and videos like Netflix, Amazon Prime Video, Twitter, Face book, Share Chat, etc. are gaining more popularity day by day. With the increasing availability of Internet all over the world, the usage of these applications is increasing rapidly. So, these applications requires quality assessment to be done on their content so that they can provide quality content on their platform. This helps to improve customer visual experience on their respective plat- forms. The main aim of image quality assessment is to quantitatively measure the perceived quality of digital and natural photographs. The acquisition, transmission, storage, post-processing, or compression of images brings different distortions, such as Gaussian blur (GB), Gaussian white noise (WN), or blocking artifacts. WN is added while taking pictures at night with a mobile, GB occurs if not focusing correctly before taking the shot. Author α σ : University Visvesvaraya College of Engineering. e-mails :dilipcd1997@gmail.com , venkateshm.uvce@bub.ernet.in Based on IQA results, decisions can be taken on compression ratio for these digital images before storing them in servers for streaming purpose as well as deciding which image will be good to be published on the online platform. A dependable IQA technique can help assess the quality of photos downloaded from the web, as well as measure the accuracy of image processing techniques precisely, such as super- resolution and image compression from a human’s perspective. The IQA algorithms are categorized into 3 groups, based upon the usage of reference image: no reference IQA (NR-IQA), reduced-reference IQA (RR- IQA) and full-reference IQA (FR-IQA). The performance of these algorithms is NR-IQA, RR-IQA, and FR-IQA, in order of increasing accuracy. However, since pristine images are not available in most of the real time situation, NR-IQA is most suitable method. The image quality assessed using no-reference (NR) IQA algorithms does not require knowledge of the original image. The image quality assessed using reduced- reference (RR) IQA methods requires only a few details about the original image. Full-reference (FR) algorithms need both a distorted image and a reference image as input and produce a quality rating for the distorted image in comparison to the original image. The most common technique to FR-IQA is to first calculate the local pixel-wise differences between reference image and distorted image. Finally, combine these local calculations into a single scalar value to represent the overall quality difference. Example of FR-IQA algorithms are: Structural Similarity Index Mean (SSIM), the peak signal-to-noise ratio (PSNR) and mean-squared error (MSE). Unlike FR-IQA, in NR-IQA the quality is measured using the features obtained from the distorted images and the subjective quality scores. II. R elated W ork This section provides a brief detail of the exisiting no-reference and reference image quality assessment techniques. Li et al. [1] proposed a new multiscale directional transform, basically a shearlet transform used to extract simple features from distorted images. Then these primary features are used to explain the nature of original images and distorted images. Then, stacked autoencoders are used to amplify the primary features and make them more distinguishable. I Global Journal of Computer Science and Technology Volume XXII Issue I Version I 17 ( )D © 2022 Global Journals Year 2022 :