Objective Image and Video Quality Assessment (I/VQA) measures predict image/video quality as perceived by human beings the ultimate consumers of visual data. Existing research in the area is mainly limited to benchmarking and monitoring of visual data. The use of I/VQA measures in the design and optimization of image/video processing algorithms and systems is more desirable, challenging and fru...

We study a variety of image deblocking algorithms for improving quality of visual signals degraded by blocking artifacts from compression. The image improvements afforded by these algorithms is measured using the PSNR, PSNR-B, and SSIM. Rather than relying on PSNR, which correlates poorly with subjective judgment, we propose a new Deblocking quality index that is sensitive to blocking artifacts...

Recently, sparse representation based methods have proven to be successful towards solving image restoration problems. The objective of these methods is to use sparsity prior of the underlying signal in terms of some dictionary and achieve optimal performance in terms of mean-squared error, a metric that has been widely criticized in the literature due to its poor performance as a visual qualit...

A blind/no-reference (NR) method is proposed in this paper for image quality assessment (IQA) of the images compressed in discrete cosine transform (DCT) domain. When an image is measured by structural similarity (SSIM), two variances, i.e. mean intensity and variance of the image, are used as features. However, the parameters of original copies are actually unavailable in NR applications; henc...

Estimating virtual CT(vCT) image from MRI data is in crucial need for medical application due to the relatively high dose of radiation exposure in CT scan and redundant workflow of both MR and CT. Among the existing work, the fully convolutional neural network(FCN) shows its superiority in generating vCT of high fidelity which merits further investigation. However, the most widely used evaluati...

in this paper, an optimal algorithm is presented for de-noising of medical images. the presented algorithm is based on improved version of local pixels grouping and principal component analysis. in local pixels grouping algorithm, blocks matching based on l2 norm method is utilized, which leads to matching performance improvement. to evaluate the performance of our proposed algorithm, peak sign...

Full-reference image quality assessment methods seek to measure visual similarity between two images (in practice, one original and the other its altered version). It has been established that traditional methods, such as Mean Square Error and Peak Signal-to-Noise Ratio poorly mimic the human visual system and much of the recent research in image quality assessment has been directed toward deve...

Alireza Avanaki [email protected] (my ID is my last name) Abstract The SSIM-optimized exact global histogram specification (EGHS) is shown to converge in the sense that the first order approximation of the result’s quality (i.e., its structural similarity with input) does not decrease in an iteration, when the step size is small. Each iteration is composed of SSIM gradient ascent and basic EGHS wi...