No-reference image quality assessment using Gabor-based smoothness and latent noise estimation
01 Nov 2017-pp 1-6
TL;DR: The algorithm employs an extension of gradient-based SSIM into the no-reference application using SVD-based AWGN estimation, and defines attributes such as Gabor-based smoothness and contrast deviation, and arrives at an overall quality score by computing a linear weighted summation of the three image attributes.
Abstract: No-reference image quality assessment is a challenging task due to the absence of a reference image in practical situations to quantify image quality. This paper proposes a new no-reference image quality metric for natural images using latent noise estimation, Gabor response, and contrast deviation. The algorithm employs an extension of gradient-based SSIM into the no-reference application using SVD-based AWGN estimation, and defines attributes such as Gabor-based smoothness and contrast deviation. The proposed metric arrives at an overall quality score by computing a linear weighted summation of the three image attributes. The proposed algorithm has been tested on several public databases (i.e. LIVE, TID 2013 and CSIQ), and the overall results display a noteworthy correlation of nearly 80% with the human visual system.
Citations
More filters
09 Nov 2020
TL;DR: By training a support vector machine (SVM) classifier using curvelet coefficient features of images of varying naturalness, a framework to predict no-reference glossiness index for any general query image is proposed and validated.
Abstract: Distinguishing artwork from digital photographs is a simple task for a human observer. However, imparting this ability to a machine requires quantification of the quality of naturalness. In particular, the quality of ‘glossiness’ of a scene in a digital photograph as opposed to that in artwork or a painting of the same scene, is a challenging parameter to quantify. In this paper, a classification-based approach is proposed to quantify this glossiness of an image. The authors propose and validate the hypothesis that features extracted from the curvelet transform contain information regarding glossiness of an image. By training a support vector machine (SVM) classifier using curvelet coefficient features of images of varying naturalness, a framework to predict no-reference glossiness index for any general query image is proposed. The reliability of the proposed metric was then gauged by obtaining its correlation with subjective scores on naturalness provided by human observers. Results exhibit noteworthy performance of this metric in representing image glossiness, and could be further improved by using a larger training database and advanced classifiers.
1 citations
01 Mar 2019
TL;DR: Noise reduction techniques are applied on Estampage images, which are the exact replica of inscriptions and are the source of the authors' ancient history to preserve and processing for future.
Abstract: Noise reduction has an impact on preprocessing and quality enhancement of images. In this paper noise reduction techniques are applied on Estampages. Estampages are the exact replica of inscriptions and are the source of our ancient history. In preserving and processing for future, image processing techniques are applied. Noise reduction is mainly accomplished using filters, in our study various filters like Median, Gaussian, Wiener, Gabor, Box filtering, order statistics, morphological operations are applied and comparative study is made with respect to Structural similarity index. Suitable filters are combined with morphological operations to draw inference. Hybrid systems for noise reduction having Erosion combined with Wiener/Median followed by dilation processes are applied to test Estampage images. Visible and comparative studies using SSIM have evidenced good result for Erosion-Median-Dilation & Erosion-Wiener-Dilation combinations.
References
More filters
TL;DR: A novel no-reference metric that can automatically quantify ringing annoyance in compressed images is presented and shows to be highly consistent with subjective data.
Abstract: A novel no-reference metric that can automatically quantify ringing annoyance in compressed images is presented. In the first step a recently proposed ringing region detection method extracts the regions which are likely to be impaired by ringing artifacts. To quantify ringing annoyance in these detected regions, the visibility of ringing artifacts is estimated, and is compared to the activity of the corresponding local background. The local annoyance score calculated for each individual ringing region is averaged over all ringing regions to yield a ringing annoyance score for the whole image. A psychovisual experiment is carried out to measure ringing annoyance subjectively and to validate the proposed metric. The performance of our metric is compared to existing alternatives in literature and shows to be highly consistent with subjective data.
150 citations
"No-reference image quality assessme..." refers background in this paper
...Therefore, most of the no-reference image quality metrics follow a feature-based approach which is based on generating a quality score in accordance to a particular type of distortion present in the image such as blockiness [7], blur identification and estimation [8] and ringing [9]....
[...]
TL;DR: A new effective noise level estimation method is proposed on the basis of the study of singular values of noise-corrupted images, which can reliably infer noise levels and show robust behavior over a wide range of visual content and noise conditions.
Abstract: Accurate estimation of Gaussian noise level is of fundamental interest in a wide variety of vision and image processing applications as it is critical to the processing techniques that follow. In this paper, a new effective noise level estimation method is proposed on the basis of the study of singular values of noise-corrupted images. Two novel aspects of this paper address the major challenges in noise estimation: 1) the use of the tail of singular values for noise estimation to alleviate the influence of the signal on the data basis for the noise estimation process and 2) the addition of known noise to estimate the content-dependent parameter, so that the proposed scheme is adaptive to visual signals, thereby enabling a wider application scope of the proposed scheme. The analysis and experiment results demonstrate that the proposed algorithm can reliably infer noise levels and show robust behavior over a wide range of visual content and noise conditions, and that is outperforms relevant existing methods.
143 citations
Book•
01 Jan 1978
116 citations
"No-reference image quality assessme..." refers methods in this paper
...By normalizing the average SRCC values over unity, the normalized weights of the three quality indices were found to be: W1 = 0.33, W2 = 0.32 and W3 = 0.34....
[...]
...The SRCC value of the four quality index for the three image database is shown below in Table I. Table I shows remarkable accuracy (> 80...
[...]
...The value of SRCC values of the proposed metric for two test databases along with other no-reference quality metrics is shown in Table II....
[...]
...In order to verify the effectiveness of the proposed quality metric, the SRCC value of the proposed metric is compared with other state-of-the-art no-reference image quality metrics viz....
[...]
...The average SRCC for all training and test images was found to be approximately 0.80....
[...]
TL;DR: It was found that mean-square-error is not a good predictor of distortion thresholds or suprathreshold perceived distortion, and mean intensity, which is not accounted for in the JPEG algorithm, plays a significant role in perceived distortion.
Abstract: Two experiments for evaluating psychophysical distortion metrics in Joint Photographic Experts Group (JPEG) encoded images are described. The first is a threshold experiment, in which subjects determined the bit rate or level of distortion at which distortion was just noticeable. The second is a suprathreshold experiment in which subjects ranked image blocks according to perceived distortion. The results of these experiments were used to determine
the predictive value of a number of computed image distortion metrics. It was found that mean-square-error is not a good predictor of distortion thresholds or suprathreshold perceived distortion. Some simple pointwise measures were in good agreement with psychophysical data; other more computationally intensive metrics involving spatial properties of the human visual system gave mixed results. It was determined that mean intensity, which is not accounted for in the JPEG algorithm, plays a significant role in perceived distortion.
73 citations
"No-reference image quality assessme..." refers background in this paper
...Very early research considered proposed to model the HVS with the help of various types of filters that are designed to mimic the contrast sensitivity function (CSF) of the human eye and extract features in accordance with the visual system [1]-[2]....
[...]
TL;DR: Experiments on various JPEG compressed images with various bit rates demonstrated that the proposed blocking artifacts measuring value matches well with the subjective image quality judged by human observers.
Abstract: Block based transform coding is one of the most popular techniques for image and video compression. However it suffers from several visual quality degradation factors, most notably from blocking artifacts. The subjective picture quality degradation caused by blocking artifacts, in general, does not agree well with the popular objective quality measure such as PSNR. A new image quality assessment method that detects and measures strength of blocking artifacts for block based transform coded images is proposed. In order to characterize the blocking artifacts, we utilize two observations: if blocking artifacts occur on the block boundary, the pixel value changes abruptly across the boundary and the same pixel values usually span along the entire length of the boundary. The proposed method operates only on a single block boundary to detect blocking artifacts. When a boundary is classified as having blocking artifacts, corresponding blocking artifact strength is also computed. Average values of those blocking artifact strengths are converted into a single number representing the subjective image quality. Experiments on various JPEG compressed images with various bit rates demonstrated that the proposed blocking artifacts measuring value matches well with the subjective image quality judged by human observers.
39 citations
"No-reference image quality assessme..." refers background in this paper
...Therefore, most of the no-reference image quality metrics follow a feature-based approach which is based on generating a quality score in accordance to a particular type of distortion present in the image such as blockiness [7], blur identification and estimation [8] and ringing [9]....
[...]