Hamid R. Sheikh's research while affiliated with Institute of Electrical and Electronics Engineers and other places
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Publications (36)
Deep neural networks targeting stereo disparity estimation have recently surpassed the performance of hand-crafted traditional models. However, training these networks require large labeled databases for obtaining accurate disparity estimates. In this paper, we address the large data requirement by generating synthetic data using natural image stat...
Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a sc...
This chapter examines objective criteria for the evaluation of image quality as perceived by an average human observer. The focus is on image fidelity, i.e., how close an image is to a given original or reference image. This paradigm of image quality assessment (QA) is also known as full reference image QA. Three classes of image QA algorithms that...
Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem an...
![Fig. 4. Steerable pyramid decomposition [22] of image (highpass...](profile/Eero-Simoncelli/publication/3328213/figure/fig4/AS:670714273230854@1536922152474/Steerable-pyramid-decomposition-22-of-image-highpass-residual-band-not-shown-A-set_Q320.jpg)
We propose the concept of quality-aware image , in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distort...
The need for efficient joint source-channel coding (JSCC) is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical JSCC schemes is a distortion model that can predict the quality of compressed digital multimedia such as images and videos. The usual approach in the JSCC li...
Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or s...
Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity...
This chapter presents a framework for doing full-reference image quality assessment based on information fidelity, which is an information-theoretic setup using natural scene statistics. It explores the relationship between image information and visual quality and presents two methods for full-reference image quality assessment. The information fid...
Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a "reference" or "perfect" image. The obvious limitation of this approach is that the...
It is widely believed that the statistical properties of the natural visual environ-ment play a fundamental role in the evolution, development and adaptation of the human visual system (HVS). An important observation about natural image sig-nals is that they are highly structured. By "structured signal", we mean that the signal samples exhibit stro...
This paper presents novel techniques for detecting watermarks in images in a known-cover attack framework using natural scene models. Specifically, we consider a class of watermarking algorithms, popularly known as spread spectrum-based techniques. We attempt to classify images as either watermarked or distorted by common signal processing operatio...
Digital images and video are prolific in the world today owing to the ease of acquisition, processing, storage and transmission. Many common image processing operations such as compression, dithering and printing affect the quality of the image. Advances in sensor and networking technologies, from the internet to wireless networks, has led to a sur...
Measurement of visual quality is crucial for many image and video processing applications. Traditionally, quality assessment (QA) algorithms predict visual quality by comparing a distorted signal against a reference, typically by modeling the Human Visual Sys-tem (HVS). In this paper, we adopt a new paradigm for video quality assessment that is an...
The need for efficient joint source-channel coding is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical joint source-channel coding schemes is a distortion model to measure the quality of compressed digital multimedia such as images and videos. Unfortunately, models f...
Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information...
Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information...
Measurement of image quality is crucial for many image-processing algorithms. Traditionally, image quality assessment algorithms predict visual quality by comparing a distorted image against a reference image, typically by modeling the human visual system (HVS), or by using arbitrary signal fidelity criteria. We adopt a new paradigm for image quali...
Measurement of image quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement and reproduction. Traditionally, researchers in image quality assessment have focused on equating image quality with similarity to a 'reference' or 'perfect' image. The field of blind, or no-reference, quality ass...
Objective methods for assessing perceptual image quality traditionally attempt to quantify the visibility of errors (di#erences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information fro...
Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available.
Measurement of image quality is crucial for many imageprocessing algorithms, such as acquisition, compression, restoration, enhancement and reproduction. Traditionally, image quality assessment algorithms have focused on measuring image fidelity, where quality is measured as fidelity with respect to a `reference' or `perfect' image. The field of bl...
Lossy video compression methods often rely on modeling the abilities and limitations of the intended receiver, the human visual system (HVS), to achieve the highest possible compression with as little effect on perceived quality as possible. Foveation, which is non-uniform resolution perception of the visual stimulus by the HVS due to the non-unifo...
Digital video data, stored in video databases and distributed through communication networks, is subject to various kinds of distortions during acquisition, compression, processing, transmission and reproduction. For example, lossy video compression techniques, which are almost always used to reduce the bandwidth needed to store or transmit video d...
Lossy video compression methods often rely on modeling the abilities and limitations of the intended receiver, the Human Visual System (HVS), to achieve the highest possible compression with as little e#ect on perceived quality as possible. Foveation, which is nonuniform resolution perception of the visual stimulus by the HVS due to the non-uniform...
Multipoint videoconferencing (MPVC) involves three or more participants engaged in video communication over a network. A video server combines the video streams from each participant and then broadcasts the resulting stream to all participants. In this paper, we propose to use foveation, which is non-uniform resolution representation of an image re...
Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. T...
Video coding techniques employ characteristics of the human visual system (HVS) to achieve high coding efficiency. Lee (2000) and Bovik have exploited foveation, which is a non-uniform resolution representation of an image reflecting the sampling in the retina, for low bit-rate video coding. We develop a fast approximation of the foveation model an...
A Very Long Instruction Word (VLIW) processor and a superscalar processor can execute multiple instructions simultaneously. A VLIW processor depends on the compiler and programmer to find the parallelism in the instructions, whereas a superscaler processor determines the parallelism at runtime. This paper compares TI TMS320C6700 VLIW digital signal...
AVery Long Instruction Word #VLIW# processor and a superscalar processor can execute multiple instructions simultaneously. A VLIW processor depends on the compiler and programmer to #nd the parallelism in the instructions, whereas a superscaler processor determines the parallelism at runtime. This paper compares TI TMS320C6700 VLIW digital signal p...
Not available Electrical and Computer Engineering
Citations
... We test our approach on 4 standard benchmarks: Set5 [49], Set14 [50], BSD100 [51] and Ubran100 [52]. The SISR results are evaluated with PSNR [53] and SSIM [54] on Y channel in the transformed YCbCr space. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/672810619912192-1537421960941_Q64/Alan-Bovik.jpg)
... Datasets. All methods are trained on two public datasets, Kalantari's dataset [6] and Hu's dataset [4]. Kalantari's dataset is captured in the real world, including 74 training and 15 testing samples. ...
... Modeling the visual physiological and psychological features of the HVS system through which the algorithms attempt to evaluate the image quality. The problem of evaluating the image quality is dealt with by the methods of the full reference image quality with it as a problem in the accuracy of the information [13,14]. The full reference scale is suitable for comparing the image coding scheme. ...
... For example, in terms of video communication, a perfect IQM can be deployed as a benchmark for measuring other IQMs when assessing a particular task. We can select the best IQM algorithm based on performance [33,35,50,51]. Moreover, in the field of medical image analysis, with the assistance of computer vision, clinicians can improve their confidence when diagnosing patients. ...
... The Red denotes the best, the blue denotes the second, and the green denotes the third. and four non-reference underwater quality evaluation indicators: peak signal-to-noise ratio (PSNR) (Jagalingam & Hegde, 2015), structural similarity (SSIM) (Wang, Bovik, Sheikh, & Simoncelli, 2004), mean square error(MSE), feature similarity (FSIM) (Zhang, Zhang, Mou, & Zhang, 2011), underwater color image quality evaluation (UCIQE) (Yang & Sowmya, 2015), underwater image quality measure (UIQM) (Panetta, Gao, & Agaian, 2015), contrast enhancement image quality (CEIQ) (Fang et al., 2014) and natural image quality evaluator(NIQE) (Mittal, Soundararajan, & Bovik, 2012). ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/273798444351497-1442290044246_Q64/Eero-Simoncelli.jpg)
... VIF criterion quantifies the Shannon information that is shared between the reference and distorted images relative to the information contained in the reference image itself. It uses natural scene statistics modelling in conjunction with an image-degradation model and an HVS model Seshadrinathan et al., 2005). Visual Information Fidely (VIF) utilises the Gaussian scale mixture (GSM) model for wavelet NSS. ...
... We selected the evaluation metrics based on the findings of Mason et al. [19] looking into the gap between commonly used image quality metrics and expert human evaluations of image quality. Despite their low correlation to perceptual quality, we used the metrics of MSE and SSIM due to their popularity, and we have also used the more complex Visual Information Fidelity (VIF) metric [42] since it has been shown to correlate well with human perceptual quality. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test [43] with a threshold of 0.05. ...
... In this scheme, we directly try to minimize the perceptual difference between the resulting output image and its ground-truth. We utilize a common perceptually oriented metric for training and evaluation: multi-scale structural similarity metric (MS-SSIM) [50]. MS-SSIM is defined as (13) where functions l, c, ands are from the SSIM definition [51], M = 5, C 1,2,3 are constants, α M = 0.1333, and β j = γ j and is equal to the associated scale power factor from the set {0.0448, 0.2856, 0.3001, 0.2363, 0.1333} all derived from the original work [50]. ...
... In the context of dehazing, higher quality means less haze present in the image and more objects that can be distinguished. Depending on the availability and/or type of the reference image, objective assessment metrics can be divided into three groups: full reference metrics [41][42][43][44][45][46][47] , reduced reference metrics [48][49][50][51] and non-reference metrics 17,[52][53][54][55][56][57] . When the reference image (haze-free image) is available, the metrics usually employed to evaluate the performance of dehazing algorithms are the full reference ones. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/272817065295878-1442056065390_Q64/Thrasyvoulos-Pappas.jpg)
... Our proposed LIVENet achieves the highest PSNR and the lowest MAE on both datasets, indicating that its results are closest to the ground truth. LIVENet can recover structures (shown by higher SSIM [39]), suppress noise, and retrieve color (shown by higher PSNR). Its output images are more realistic (shown by lower MAE) and best aligned with the human vision (shown by low LPIPS [48]). ...
