A Perceptual Image Sharpness Metric Based on Local Edge Gradient Analysis

Feichtenhofer, Christoph; Fassold, Hannes; Schallauer, Peter

doi:10.1109/lsp.2013.2248711

Cited by 103 publications

(52 citation statements)

References 14 publications

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“…To be consistent with visual inspection, an IQM should demonstrate a monotonic dependence on the level of degradation of an image and exhibit small variations for different images with equivalent levels of degradation [34]. A scatter plot is used to test the prediction monotonicity.…”

Section: Prediction Monotonicitymentioning

confidence: 99%

Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images

Yang

2017

IJGI

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Image quality assessment plays an important role in image processing applications. In many image applications, e.g., image denoising, deblurring, and fusion, a reference image is rarely available for comparison with the enhanced image. Thus, the quality of enhanced images must be evaluated blindly without references. In recent years, many no-reference image quality metrics (IQMs) have been proposed for assessing digital image quality. In this paper, we first review 21 commonly employed no-reference IQMs. Second, we apply these measures to Quickbird images with three different types of general content (urban, rural, and harbor) subjected to three types of degradation (average filtering, Gaussian white noise, and linear motion degradation), each with 40 degradation levels. We evaluate the robustness of the IQMs based on the criteria of prediction accuracy, prediction monotonicity, and prediction consistency. Then, we perform factor analysis on those IQMs deemed robust, and cluster them into several components. We then select the IQM with the highest loading coefficient as the representative IQM for that component. Experimental results suggest that different measures perform differently for images with different contents and subjected to different types of degradation. Generally, the degradation method has a stronger effect than the image content on the evaluation results of an IQM. The same IQM can provide opposite dependences on the level of degradation for different degradation types, and an IQM that performed well with one type of degradation may not perform well with another type. The training-based measures are not appropriate for remote sensing images because the results are highly dependent on the samples employed for training. Only seven of the 21 IQMs were found to fulfill the requirements of robustness. Edge intensity (EI) and just noticeable distortion (JND) are suggested for evaluating the quality of images subjected to average filter degradation. EI, blind image quality assessment through anisotropy (BIQAA), and mean metric (MM) are suggested for evaluating the quality of images subjected to Gaussian white noise degradation. Laplacian derivative (LD), JND, and standard deviation (SD) are suggested for evaluating the quality of images subjected to linear motion. Finally, EI is suggested for evaluating the quality of an image subjected to an unknown type of degradation.

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Section: Prediction Monotonicitymentioning

confidence: 99%

Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images

Yang

2017

IJGI

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“…The derived HVS-based sharpness perception model is used to predict the relative perceived sharpness in images with different content [39]. Later more and more research is based on the JNB [40], [41], [42], [43], [44]. The other way to predict a certain distortion is transform-based, such as Discrete cosine transform (DCT) [45] and Discrete wavelet transform (DWT) [46], [47].…”

Section: ) Full Reference Metricsmentioning

confidence: 99%

Evaluation of image quality metrics for sharpness enhancement

Cheng

Pedersen

Chen

2017

Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis

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Abstract-Image quality assessment has become a meaningful research field due to the explosive growth of image processing technologies in imaging industries. It is becoming more usual to quantify the quality of an image using image quality metrics, rather than carrying out time-consuming psychometric experiments. However, there is little research on the performance of image quality metrics on quality enhanced images. In this paper, we focus on images that have been enhanced by sharpening. A psychometric experiment was designed with observers giving scores to different images enhanced by sharpening on a display in a controlled dark environment. The results showed that full reference image quality metrics performed well when sharpening did not improve the visual image quality, while in images where sharpening increased the visual quality the performance was lower. No reference image quality metrics show better predictions than full reference image quality metrics in most cases.

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“…An indirect measurement is to analyze the statistics of the local edge gradients [8] and model the gradient image as a Markov chain [9]. Blurriness was also modeled as the loss of energy at high frequencies and measured from the local power of the high-frequency wavelet coefficients [10], the log-energy of the discrete wavelet transform subbands [11], and the image effective bandwidth [12].…”

Section: ) Artifact Measurementmentioning

confidence: 99%

“…The features of kurtosis, smoothness, and sharpness quantify the distortion based on the statistical properties of band 8 1 . Histo-noise and MJSD quantify the similarity between frequency bands based on the probability density functions of 8 .L. c,.…”

Section: A Frame-level Feature Extractionmentioning

confidence: 99%

No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis

Zhu

Asari

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A discrete cosine transform (DCT)-based no-reference video quality prediction model is proposed that measures artifacts and analyzes the statistics of compressed natural videos. The model has two stages: 1) distortion measurement and 2) nonlinear mapping. In the first stage, an unsigned ac band, three frequency bands, and two orientation bands are generated from the DCT coefficients of each decoded frame in a video sequence. Six efficient frame-level features are then extracted to quantify the distortion of natural scenes. In the second stage, each frame-level feature of all frames is transformed to a corresponding video-level feature via a temporal pooling, then a trained multilayer neural network takes all video-level features as inputs and outputs, a score as the predicted quality of the video sequence. The proposed method was tested on videos with various compression types, content, and resolution in four databases. We compared our model with a linear model, a support-vector-regression-based model, a state-of-the-art training-based model, and a four popular full-reference metrics. Detailed experimental results demonstrate that the results of the proposed method are highly correlated with the subjective assessments. IndexTerms-Blocking artifact, discrete cosine transform (DCT), H.264/Advanced Video Coding (AVC), natural scene, no-reference (NR) measure, video quality assessment (VQA).

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A Perceptual Image Sharpness Metric Based on Local Edge Gradient Analysis

Cited by 103 publications

References 14 publications

Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images

Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images

Evaluation of image quality metrics for sharpness enhancement

No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis

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