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No Reference Quality Assessment Essay

2078 words - 9 pages

In web applications, images and videos are transmitted over transmission networks of all kinds. For efficient use of band width and also storage memory space, images are compressed mainly using lossy compression techniques. The result of lossy compression is introduction of artifacts such as blockiness, ringing and blurring which degrades quality of the images. The channel noise also contributes in quality loss. In the field of video processing and image processing image quality assessment is essential and challenging problem. Image quality assessment plays a vital role to evaluate various image processing algorithm and systems. There are enormous examples like image acquisition, storage, retrieval, watermarking, authentication, enhancement, compression and synthesis. So there is an immense need of quantification of image quality [1]. An Image quality assessment is done by two approaches viz Subjective, in which a group of experts and non experts are asked to rate the quality and a score is obtained by taking mean of subject rating. Best way to assess the quality of image is to see it because human being is ultimate receiver in many image processing applications. The subjective evaluation, Mean Opinion Score (MOS) is reliable but it is slow, inconvenient and time-consuming [2]. Differential MOS (DMOS) is also used. The scores given by subjects were converted to different score i.e. difference between the test and the reference image and then converted to Z-score. This scores are scaled to 1-100 range and it is called as differential mean opinion score (DMOS) [3]. Objective evaluation has aim to predict perceived image quality by human being.
Objective image quality assessment is further categorized into Full Reference (FR), Reduced Reference (RR) and No Reference (NR) [4]. In FR original image is used as reference to assess the quality of degraded image. This FR based quality metrics are mainly used in assessing the performance of the channel and compression schemes. The commonly used metrics are peak signal to noise ratio (PSNR), Image fidelity, Correlation coefficient, structural content [5]. PSNR is simple and widely accepted, but also criticized for not correlating with perceived quality measurement [6]. Human visual system based, mean structural similarity index (MSSIM) is based on the theme that, structural information of an image changes if the image is distorted [7, 8]. In RR instead of original image certain features of it are available to assess the quality of reconstructed image. The features are also used for the image repair/ impairment [9]. The feature calculated as wavelet coefficients of distorted image images fitted with Gaussian scale mixture (GSM) distributions. The algorithm computes the average difference between scaled entropies of wavelet coefficients of reference and distorted images [10]. Also another approach is by structural similarity approach. In this approach statistical features are extracted from multiscale...

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