Review on Deep CNN-Based Blind Image Quality Prediction Techniques

Authors: Chhaya Nayak

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Abstract

The images that we take from the various image processing applications usually need to be evaluated with their quality attribute todecide whether they are suitable for specific applications or not. Blind image quality assessment (BIQA) is one of the methods which aim to predict quality of images as observed by humans while not access to reference image victimization Deep CNN. With the increasing demand for image-Processing applications, the efficient and reliable evaluation of image quality has increased in importance. Measuring the image quality is of basic importance for various image processapplications;wherever the goal of image quality assessment (IQA) ways is to mechanicallyevaluatethe standard ofimages in agreement with human quality judgments. Various IQA methods have been proposed over the past years to fulfil this goal. In this paper, a survey of the image quality assessment methods for image processing applications images is presented.

Introduction

Image quality assessment plays vital role in image processing applications such as image compression, image restoration image enhancement and other fields. IQA is also useful for the applications such as image reconstruction and image retrieval. Image quality assessment (IQA) is very important for the image applications because sometime images may contain various types of noise like blur, noise, contrast change etc.IQA dataset gathering is based on complicated and timeconsuming psychometric experiments. The cost of generating datasets for IQA is high since it requires supervision of expert. Therefore, the fundamental IQA benchmarks are comprised of solely a few thousands of records. The latter complicates the creation of deep learning models because they require large amounts of training samples to generalize. Image quality assessment (IQA) classification Image quality assessment (IQA) can be broadly categorized into subjective and objective quality assessment (QA). In subjectiveQA, humans are supposed to evaluate the visual quality of content and the average of subjective ratings is termed as Mean Opinion Score (MOS). Subjective QA is most reliable method of quantifying perceptual quality of content because in most cases such content is meant to be viewed by humans. However, subjective QA method is time consuming, expensive, and cannot be embedded in image processing algorithms for optimization purposes. While in case of objective quality assessment automatically predicts the quality of images as perceived by humans. Significant progress has been made in the last two decades in the design of objective QA methods andbased on the IQA three major frameworks are now wellestablished

Conclusion

In this work, we carried out a review on performance evaluation study in the field of IQA. This paper presents a systematic survey of various DNNbasedmethods for BIQA .This classification strategy explicitly shows the characteristics, advantages and disadvantages ofdifferent DNN methods for BIQA.I hope this survey of DNN methods can serveas a useful reference towards a better understanding of thisresearch field.