Image quality assessment has involved the comparison of a corrupted image with an original or perfect version of that given image. Many real time cases, this perfect image is not easily available. This research introduces a new metric, that measures visual quality of a single given image and also quality of video images is considered. Operating in this no-reference framework, one new method is suited for real-world applications, such as television monitoring and digital camera quality sensing. Most of the theoretical basis of this work centers on the notion of level-of-detail. Knowing whether an image is very smooth or highly detailed is important in both the detection and assessment of errors. At this time, there are three types of errors that commonly arise in practice are considered, that are namely blur, noise, and compression. Every given image is assigned a score reflecting its perceived quality. Human test cases may validate the new techniques. In this paper, we discuss several open challenges in an image and video quality research. These challenges coming from lack of complete perceptual models for: supra threshold distortions, natural images, interactions between images and distortions, images containing nontraditional and multiple distortions, and images containing enhancements. Here we also discuss the challenges related to computational efficiency.

Image Quality Assessment (IQA), NR Method, objective QA

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