Reduced reference image and video quality assessments: review of methods

Dost, Shahi; Saud, Faryal; Shabbir, Maham; Khan, Muhammad Gufran; Shahid, Muhammad; Lövström, Benny

doi:10.1186/s13640-021-00578-y

Cited by 13 publications

(4 citation statements)

References 118 publications

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“…On the other hand, Reduced-Reference Image Quality Assessment (RR-IQA) methods do not require a full reference image to assess the quality of a distorted image. Rather, they only need some information and features from the reference image [3], [4]. Assessing the quality of images in the real world can be challenging as the reference image is often not available.…”

Section: Introductionmentioning

confidence: 99%

No Reference Image Quality Assessment Based on DCT and SOM Clustering

Zamani,

Azar

2024

IEEE Access

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The quality of images and videos is an important research topic due to their wide applications. The research should match the human subjective evaluation of quality. There are three types of objective image quality assessment: full-reference, reduced-reference, and no-reference. Noreference image quality assessment is the most realistic because distorted images often have no reference. In this article, we propose an algorithm that uses the discrete cosine transform (DCT) of patches to extract a feature vector. We apply the Self Organizing Map (SOM) in two levels. In the first level, we classify the patches into three types: good quality, noisy, and blurred. In the second level, we further classify the noisy and blurred patches into weak and severe distortions using two separate SOMs. Then, we used a straightforward neural network with supervised back-propagation to adjust the number of distorted patches of five classes in one image to assign a quality score. Our method of training Self-Organizing Maps (SOMs) with reference, noisy, and blurred image patches allowed us to estimate quality scores of other types of distortions that matched subjective scores equally well. The Spearman Rank Order Correlation Coefficient (SROCC) performance of our method, when measured against the subjective scores of degraded images that were used in training, lies in the range of 0.88-0.92. Similarly, for other degradation types that were examined (10 in total), the SROCC performance is in the range of 0.76-0.89, which is higher than other methods. The experiments show that our scores are consistent with the subjective scores.INDEX TERMS Feature vector, Image quality assessment (IQA), No-reference image quality assessment.

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Section: Introductionmentioning

confidence: 99%

No Reference Image Quality Assessment Based on DCT and SOM Clustering

Zamani,

Azar

2024

IEEE Access

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“…In addition to being time-consuming, laborious, and inefficient, manual inspection is not good in terms of failure response speed, and surveillance video signals in various failures often cannot be found in time by operation and maintenance personnel, which results in a loss of data or loss of video quality [6]. Also, manual inspection has certain limitations and instabilities caused by people's lack of concentration, fatigue or other factors, so that such manual inspection results are not objective [7]. On the other hand, due to the limited number of displays, maintenance personnel often monitor multiple cameras at the same time in a monitor screen or randomly extract the camera display, resulting in some monitoring points being missed or ignored [8].…”

Section: Introductionmentioning

confidence: 99%

A Reactive Deep Learning-Based Model for Quality Assessment in Airport Video Surveillance Systems

Liu,

Pan,

Fan

2024

Electronics

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Monitoring the correct operation of airport video surveillance systems is of great importance in terms of the image quality provided by the cameras. Performing this task using human resources is time-consuming and usually associated with a delay in diagnosis. For this reason, in this article, an automatic system for image quality assessment (IQA) in airport surveillance systems using deep learning techniques is presented. The proposed method monitors the video surveillance system based on the two goals of “quality assessment” and “anomaly detection in images.” This model uses a 3D convolutional neural network (CNN) for detecting anomalies such as jitter, occlusion, and malfunction in frame sequences. Also, the feature maps of this 3D CNN are concatenated with feature maps of a separate 2D CNN for image quality assessment. This combination can be useful in improving the concurrence of correlation coefficients for IQA. The performance of the proposed model was evaluated both in terms of quality assessment and anomaly detection. The results show that the proposed 3D CNN model could correctly detect anomalies in surveillance videos with an average accuracy of 96.48% which is at least 3.39% higher than the compared methods. Also, the proposed hybrid CNN model could assess image quality with an average correlation of 0.9014, which proves the efficiency of the proposed method.

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“…The subjective and the objective image or video QoE metrics existing in the literature can be categorized as full reference (FR), reduced reference (RR), and no reference (NR) [9][10][11][12]. The FR ones cannot be used without the original video, and the RR ones require video features obtained from the original video.…”

Section: Introductionmentioning

confidence: 99%

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

Coskun,

Nur Yilmaz,

Battisti

et al. 2023

J. Imaging

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A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.

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Reduced reference image and video quality assessments: review of methods

Cited by 13 publications

References 118 publications

No Reference Image Quality Assessment Based on DCT and SOM Clustering

No Reference Image Quality Assessment Based on DCT and SOM Clustering

A Reactive Deep Learning-Based Model for Quality Assessment in Airport Video Surveillance Systems

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

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