No‐Reference Approaches to Image and Video Quality Assessment

Chen, Chang Wen; Dagiuklas, Tasos; Atzori, Luigi

doi:10.1002/9781118736135.ch5

Cited by 9 publications

(8 citation statements)

References 93 publications

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“…In order to objectively evaluate the aforementioned resulted images whose original HR image is not available, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) [25], [26] is selected to quantitatively measure the quality of the restored HR images. For no-reference quality assessment, BRISQUE shows strong correlations with human visual perception [18].…”

Section: Brisque Resultsmentioning

confidence: 99%

SAR Analysis for Real Video Super- Resolution Improvement

Chen

Zhou

et al. 2020

IEEE Access

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This paper presents a simultaneous autoregressive (SAR) analysis method to describe the unknown signal-to-noise ratio (SNR) and the texture feature of low-quality real video frames when the ground-truth images are not available. The real video images degraded by the factors, such as electronic noise, oversaturated pixels, motion blur, and compression artifacts, often result in poor motion registration estimation, which makes the performance of the existing video super-resolution (VSR) algorithms lower than expected. It is hard to estimate the SNR of the low-quality real frames without any prior knowledge. To solve this problem, we made a connection between SAR hyperparameters and the SNR of real images. The relationship expression of them was given in this paper. Using the proposed method, well-registered low-quality real video frames can be selected to decrease the root mean squared error (RMSE) of motion estimation of video frames for VSR reconstruction improvement. The anomalous low-quality frame images whose SAR hyperparameters values are inconsistent with others will be considered for removal. Synthetic experiments were designed to illustrate how the SAR hyperparameters values vary with the variation of synthetic parameters. In order to better illustrate the effectiveness of the proposed method, real low-quality videos captured under different conditions were tested under VSR reconstruction experiments. The VSR reconstruction results show that the results obtained using SAR prior analysis have sharper edges and fewer ringing artifacts than the original results. It indicates that the proposed method is helpful to obtain better results of motion registration estimation for low-quality real video images.

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Section: Brisque Resultsmentioning

confidence: 99%

SAR Analysis for Real Video Super- Resolution Improvement

Chen

Zhou

et al. 2020

IEEE Access

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“…blur) or a distortion-generic estimation of overall quality of the image. 16,17 Objective quality assessment of medical images has been usually criticized about its ability in estimating diagnostic quality or even visual quality of the image. 18 Although the critique might still be valid for more traditional simple metrics like entropy, but our results showed that a deeply trained CNN can evaluate the diagnostic value of CLE images correlating well with the subjective assessment done by experts.…”

Section: Discussionmentioning

confidence: 99%

Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks

et al. 2017

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Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.

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“…IQA methods may be broadly classified as reference or no-reference ("NR/blind") [10]. In simple terms, all the reference-based methods try to estimate some form of distance between the reference ("clean") image and the input/query image.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Existing IQA methods can be classified into three categories, based on the amount of the information that is available to the method: Full-Reference (FR), Reduced-Reference (RR), and No-Reference (NR) methods [10]. FR quality assessment requires both access to the distorted images or videos as well as the clean references.…”

Section: Introductionmentioning

confidence: 99%

Potential of Deep Features for Opinion-Unaware, Distortion-Unaware, No-Reference Image Quality Assessment

Mukherjee

Valenzise

Cheng

2020

Lecture Notes in Computer Science

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Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted "reference" version of the input image to compare with, in order to predict this score. However, recent "No-reference" methods circumvent this requirement by modelling the distribution of clean image features, thereby making them more suitable for practical use. However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are difficult to obtain and standardise. We explore the possibility of using deep features instead, particularly, the encoded (bottleneck) feature maps of a Convolutional Autoencoder neural network architecture. Also, we do not train the network on subjective scores (unsupervised learning). The primary requirements for an IQA method are monotonic increase in predicted scores with increasing degree of input image distortion, and consistent ranking of images with the same distortion type and content, but different distortion levels. Quantitative experiments using the Pearson, Kendall and Spearman correlation scores on a diverse set of images show that our proposed method meets the above requirements better than the state-of-art method (which uses hand-crafted features) for three types of distortions: blurring, noise and compression artefacts. This demonstrates the potential for future research in this relatively unexplored sub-area within IQA.

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No‐Reference Approaches to Image and Video Quality Assessment

Cited by 9 publications

References 93 publications

SAR Analysis for Real Video Super- Resolution Improvement

SAR Analysis for Real Video Super- Resolution Improvement

Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks

Potential of Deep Features for Opinion-Unaware, Distortion-Unaware, No-Reference Image Quality Assessment

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