Reduced Reference Quality Assessment of Light Field Images

Paudyal, Pradip; Battisti, Federica; Carli, Marco

doi:10.1109/tbc.2019.2892092

Cited by 86 publications

(34 citation statements)

References 40 publications

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“…They used spatial frequency for the variations of the sensitivity to contrast and texture. A new model for QoE evaluation of the new and advanced multicast services for the 5G networks was deployed [ 20 ]. It uses the measure of deformity in the depth map.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

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Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

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Section: Related Workmentioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

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“…In order to demonstrate the effectiveness of our proposed NR-LFQA model, we conduct extensive experiments by using existing 2D, 3D, multi-view, and LFI quality assessment algorithms. In our experiments, we utilize ten 2D-FR metrics [17]- [20], [24]- [27], [36], three 2D-NR metrics [33]- [35], one 3D-FR metric [38], two 3D-NR metrics [40], [41], five multiview FR metrics [42]- [45], one multi-view NR metric [46], one LFI FR metric [49], and one LFI RR metric [50]. For all 2D-FR, multi-view FR, LFI FR, LF-IQM, NIQE and APT algorithms, the global predicted score of LFI is obtained by…”

Section: E Comparison With Other Objective Metricsmentioning

confidence: 99%

“…Additionally, we compare the proposed NR-LFQA with LF-IQM [50] regarding to time complexity on Win5-LID dataset. From Table VIII, we can find that the proposed method is verified to have lower computation time.…”

Section: Model Generality and Time Complexitymentioning

confidence: 99%

No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

Shi

Zhou

Chen

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.Index Terms-Light field image, quality assessment, epipolar plane image, naturalness, spatial quality, angular consistency.

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“…Therefore, it has become a popular research topic in the field of image quality evaluation in recent years. In general, objective evaluation methods include full reference [ 3 , 4 ], reduced reference [ 5 , 6 ], and nonreference [ 7 , 8 , 9 , 10 , 11 , 12 ]. This study focuses on the nonreference quality evaluation of images, i.e, referring to the original image information in image quality evaluation is unnecessary.Generally speaking, the nonreference image quality evaluation method is also called blind image quality (BIQ) evaluation method.…”

Section: Introductionmentioning

confidence: 99%

Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy

Liu

et al. 2019

Entropy

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The image quality evaluation method, based on the convolutional neural network (CNN), achieved good evaluation performance. However, this method can easily lead the visual quality of image sub-blocks to change with the spatial position after the image is processed by various distortions. Consequently, the visual quality of the entire image is difficult to reflect objectively. On this basis, this study combines wavelet transform and CNN method to propose an image quality evaluation method based on wavelet CNN. The low-frequency, horizontal, vertical, and diagonal sub-band images decomposed by wavelet transform are selected as the inputs of convolution neural network. The feature information in multiple directions is extracted by convolution neural network. Then, the information entropy of each sub-band image is calculated and used as the weight of each sub-band image quality. Finally, the quality evaluation values of four sub-band images are weighted and fused to obtain the visual quality values of the entire image. Experimental results show that the proposed method gains advantage from the global and local information of the image, thereby further improving its effectiveness and generalization.

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Reduced Reference Quality Assessment of Light Field Images

Cited by 86 publications

References 40 publications

Adaptive Reservation of Network Resources According to Video Classification Scenes

Adaptive Reservation of Network Resources According to Video Classification Scenes

No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy

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