Image Quality Assessment Framework Research Articles

Image quality assessment using two-dimensional complex mel-cepstrum

Assessment of visual quality plays a crucial role in modeling, implementation, and optimization of image- and video-processing applications. The image quality assessment (IQA) techniques basically extract features from the images to generate objective scores. Feature-based IQA methods generally consist of two complementary phases: (1) feature extraction and (2) feature pooling. For feature extraction in the IQA framework, various algorithms have been used and recently, the two-dimensional (2-D) mel-cepstrum (2-DMC) feature extraction scheme has provided promising results in a feature-based IQA framework. However, the 2-DMC feature extraction scheme completely loses image-phase information that may contain high-frequency characteristics and important structural components of the image. In this work, “2-D complex mel-cepstrum” is proposed for feature extraction in an IQA framework. The method tries to integrate Fourier transform phase information into the 2-DMC, which was shown to be an efficient feature extraction scheme for assessment of image quality. Support vector regression is used for feature pooling that provides mapping between the proposed features and the subjective scores. Experimental results show that the proposed technique obtains promising results for the IQA problem by making use of the image-phase information.

On combining visual perception and color structure based image quality assessment

The state-of-the-art image quality assessment (IQA) methods based on structure calculation are mostly implemented without considering visual color sensitivity and visual perception threshold (VPT). Actually, human visual system (HVS) is extremely sensitive to the color structure and has visual perception threshold to reduce redundant information by representing the image sparsely. Hence, through incorporating the color structure and the VPT, this paper proposed a novel IQA framework which utilizes minimum amount of structure coefficients to capture the variation of color structure and distortion of degraded image by applying a VPT to remove the visual unperceived coefficients. The difference of the proportion of visual perceived coefficients between distorted and reference image is measured to acquire image quality score. Experimental results on two benchmark databases (LIVE II and TID) indicate the rationality and validity of the proposed method.

A color intensity invariant low-level feature optimization framework for image quality assessment

Image Quality Assessment (IQA) algorithms evaluate the perceptual quality of an image using evaluation scores that assess the similarity or difference between two images. We propose a new low-level feature based IQA technique, which applies filter-bank decomposition and center-surround methodology. Differing from existing methods, our model incorporates color intensity adaptation and frequency scaling optimization at each filter-bank level and spatial orientation to extract and enhance perceptually significant features. Our computational model exploits the concept of object detection and encapsulates characteristics proposed in other IQA algorithms in a unified architecture. We also propose a systematic approach to review the evolution of IQA algorithms using unbiased test datasets, instead of looking at individual scores in isolation. Experimental results demonstrate the feasibility of our approach.

Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters.

Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA, reduced-reference (RR) IQA, and no-reference (NR) IQA according to the amount of information required from the original image. Although NR-IQA and RR-IQA are widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the fact that in many applications, such as parameter selection for image restoration algorithms, a series of distorted images are available, the authors propose a novel comparison-based IQA (C-IQA) framework. The new comparison-based framework parallels FR-IQA by requiring two input images and resembles NR-IQA by not using the original image. As a result, the new comparison-based approach has more application scenarios than FR-IQA does, and takes greater advantage of the accessible information than the traditional single-input NR-IQA does. Further, C-IQA is compared with other state-of-the-art NR-IQA methods and another RR-IQA method on two widely used IQA databases. Experimental results show that C-IQA outperforms the other methods for parameter selection, and the parameter trimming framework combined with C-IQA saves the computation of iterative image reconstruction up to 80%.

Oriented Correlation Models of Distorted Natural Images With Application to Natural Stereopair Quality Evaluation

In recent years, bandpass statistical models of natural, photographic images of the world have been used with great success to solve highly diverse problems involving image representation, image repair, image quality assessment (IQA), and image compression. One missing element has been a reliable and generic model of spatial image correlation that reflects the distributions of oriented and relatively oriented spatial structures. We have developed such a model for bandpass pristine images and have generalized it here to also capture the spatial correlation structure of bandpass distorted images. The model applies well to both luminance and depth images. As a demonstration of the usefulness of the generalized model, we develop a new no-reference stereoscopic/3D IQA framework, dubbed stereoscopic/3D blind image naturalness quality index, which utilizes both univariate and generalized bivariate natural scene statistics (NSS) models. We first validate the robustness and effectiveness of these novel bivariate and correlation NSS features extracted from distorted stereopairs, then demonstrate that they are predictive of distortion severity. Our experimental results show that the resulting 3D image quality predictor based in part on the new model outperforms state-of-the-art full- and no-reference 3D IQA algorithms on both symmetrically and asymmetrically distorted stereoscopic image pairs.

Saliency-Guided Deep Framework for Image Quality Assessment

Image quality assessment (IQA) has thrived for decades, and researchers continue to explore how the human brain perceives visual stimuli. Psychological evidence shows that humans prefer qualitative descriptions when evaluating image quality, yet most researches still concentrate on numerical descriptions. Furthermore, handcrafting features are widely used in this community, which constrains the models' flexibility. A novel model is proposed with two major advantages: the saliency-guided feature learning can learn features unsupervisedly, and the deep framework recasts IQA as a classification problem, analogous to human qualitative evaluation. Experiments validate the proposed model's effectiveness.

Saliency-guided deep framework for image quality assessment

Image quality assessment (IQA) has thrived for decades, and researchers continue to explore how the human brain perceives visual stimuli. Psychological evidence shows that humans prefer qualitative descriptions when evaluating image quality, yet most researches still concentrate on numerical descriptions. Furthermore, handcrafting features are widely used in this community, which constrains the models' flexibility. A novel model is proposed with two major advantages: the saliency-guided feature learning can learn features unsupervisedly, and the deep framework recasts IQA as a classification problem, analogous to human qualitative evaluation. Experiments validate the proposed model's effectiveness.

Image quality/distortion metric based on α-stable model similarity in wavelet domain

Quality assessment is of central importance in numerous image processing tasks. State-of-the-art objective image quality assessment (IQA) algorithms are generally devised for specific distortion types or based on training procedure of large databases. In this work, we propose a general-purpose full-reference/no-reference (FR/NR) IQA framework for image distortions, nominated by Image Quality/Distortion Metric (IQDM). The leptokurtic and heavy-tailed behaviors of image wavelet coefficients are characterized by symmetric α-stable (SαS) density, and the statistical studies indicate that the model parameters may be altered because of the presence of distortion. This important priori knowledge of original image’s distribution is then used to gauge the distortion between degraded and reference SαS models in multi-scale wavelet sub-bands. We investigate the relationship between original and degraded parameters over scales, accordingly infer the original parameters from the degraded ones. A characteristic probability density function for SαS and its closed-form Kullback–Leibler distance are derived for FR/NR-IQDM using the model parameters. Extensive experiments and comparisons demonstrate that the proposed FR/NR-IQDM scheme is efficacious to most common types of distortion, and leads to a highly comparable performance to the benchmarks and prevalent competitors in consistency with subjective judgements.

Halftone Image Quality Assessment Based on Digital Watermark

Digital watermarking is the process of computer-aided information hiding in a carrier signal with robust and concealment characteristics. A halftone image quality assessment framework based on digital watermark is proposed. First, we segmented image dark tong area and edge through C-V model and transformed segmentation image into binary image to obtain the area in which watermark is to be embedded. Then, the watermark was embedded and the image quality assessment was completed by extracted watermark. The experimental results show that the presented quality evaluation scheme of halftone image can detect printed image quality efficiently with the characteristics of novel and rapid.

Image quality assessment based on S-CIELAB model

This paper proposes a new image quality assessment framework which is based on color perceptual model. By analyzing the shortages of the existing image quality assessment methods and combining the color perceptual model, the general framework of color image quality assessment based on the S-CIELAB color space is presented. The S-CIELAB color model, a spatial extension of CIELAB, has an excellent performance for mimicking the perceptual processing of human color vision. This paper incorporates excellent color perceptual characteristics model with the geometrical distortion measurement to assess the image quality. First, the reference and distorted images are transformed into S-CIELAB color perceptual space, and the transformed images are evaluated by existing metric in three color perceptual channels. The fidelity factors of three channels are weighted to obtain the image quality. Experimental results achieved on LIVE database II shows that the proposed methods are in good consistency with human subjective assessment results.

Vision models for image quality assessment: one is not enough

A number of image quality metrics are based on psy- chophysical models of the human visual system. We propose a new framework for image quality assessment, gathering three indexes describing the image quality in terms of visual performance, visual appearance, and visual attention. These indexes are built on three vision models grounded on psychophysical data: we use models from Mantiuk et al. (visual performance), Moroney et al. (visual ap- pearance), and Itti, Koch, and Niebur (visual attention). For accuracy reasons, the sensor and display system characteristics are taken into account in the evaluation process, so that these indexes character- ize the image acquisition, processing, and display pipeline. We give evidence that the three image quality indexes, all derived from psy- chophysical data, are very weakly correlated. This emphasizes the need for a multicomponent description of image quality. © 2010 SPIE and IS&T. (DOI: 10.1117/1.3495989)

Image Quality Assessment Based on Multiscale Geometric Analysis

Reduced-reference (RR) image quality assessment (IQA) has been recognized as an effective and efficient way to predict the visual quality of distorted images. The current standard is the wavelet-domain natural image statistics model (WNISM), which applies the Kullback-Leibler divergence between the marginal distributions of wavelet coefficients of the reference and distorted images to measure the image distortion. However, WNISM fails to consider the statistical correlations of wavelet coefficients in different subbands and the visual response characteristics of the mammalian cortical simple cells. In addition, wavelet transforms are optimal greedy approximations to extract singularity structures, so they fail to explicitly extract the image geometric information, e.g., lines and curves. Finally, wavelet coefficients are dense for smooth image edge contours. In this paper, to target the aforementioned problems in IQA, we develop a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), and the Weber's law of just noticeable difference (JND). In the proposed framework, MGA is utilized to decompose images and then extract features to mimic the multichannel structure of HVS. Additionally, MGA offers a series of transforms including wavelet, curvelet, bandelet, contourlet, wavelet-based contourlet transform (WBCT), and hybrid wavelets and directional filter banks (HWD), and different transforms capture different types of image geometric information. CSF is applied to weight coefficients obtained by MGA to simulate the appearance of images to observers by taking into account many of the nonlinearities inherent in HVS. JND is finally introduced to produce a noticeable variation in sensory experience. Thorough empirical studies are carried out upon the LIVE database against subjective mean opinion score (MOS) and demonstrate that 1) the proposed framework has good consistency with subjective perception values and the objective assessment results can well reflect the visual quality of images, 2) different transforms in MGA under the new framework perform better than the standard WNISM and some of them even perform better than the standard full-reference IQA model, i.e., the mean structural similarity index, and 3) HWD performs best among all transforms in MGA under the framework.