A full reference stereoscopic video quality assessment metric

Appina, Balasubramanyam; Manasa, K; Channappayya, Sumohana S.

doi:10.1109/icassp.2017.7952509

Cited by 14 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A four-stage FR VQA algorithm called F LOSIM 3D is proposed by the work of Appina et al 60 based on the temporal, spatial and depth features of natural stereoscopic videos. In this method, the temporal quality features are extracted from both the left and the right views by an existing motion-based 2D stereoscopic VQA metric, the spatial quality features are estimated by using an existing 2D stereoscopic IQA metric for both left and the right views and the depth quality features are estimated with the help of depth maps for each frame of both left and right views.…”

Section: Fr Objective Metricsmentioning

confidence: 99%

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Biswas

Appina

Tamboli³

et al. 2021

Applications of Machine Learning 2021

Self Cite

View full text Add to dashboard Cite

Objective quality metrics provide cost-efficient methods for quality evaluation, as they are practically algorithms, models, that avoid the necessity of subjective assessment, which is a precise but resource-consuming approach. Their ultimate measure of prediction accuracy fundamentally relies on the correlation between the estimated levels of quality and the actual subjective scores of perceived quality, rated by human individuals. Such metrics have already been developed for every single emerging technology where quality, in general, is relevant. This applies to stereoscopic 3D imaging as well, which is utilized in both industry, healthcare, education and entertainment. In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. Our contribution addresses each and every state-of-the-art objective metric in the scientific literature, separately for image and video quality. The study differentiates the metrics by input requirements and supervision, and examines performance via statistical measures. Machine learning algorithms are particularly emphasized within the paper, such as the Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) using Segmented Stacked Auto-Encoder (S-SAE), different Convolutional Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. The paper focuses on the actual practical applications of the predictive models, and highlights relevant criteria, along with general feasibility, suitability and usability. The analysis of the investigated use cases also addresses potential future research questions and specifies the appropriate directives for quality-focused, user-centric development.

show abstract

Section: Fr Objective Metricsmentioning

confidence: 99%

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Biswas

Appina

Tamboli³

et al. 2021

Applications of Machine Learning 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…al) [47], Q_Shao (3D Quality metric by Shao et. al) [48], HV3D (Human Visual system based 3D quality metric for stereo video) [49], and FLOSIM3D (Flow based Similarity measure for 3D video) [77]. In addition, we follow what is considered to be a common practice in 3D quality evaluation by using PSNR (Peak Signal to Noise Ratio), SSIM (Structural SIMilarity) [31], MS-SSIM (Multi-Scale SSIM) [50], and VIF (Visual Information Fidelity) [51] for FR metric integration.…”

Section: B Integration Of Saliency Maps Into Fr Quality Metricsmentioning

confidence: 99%

Saliency inspired quality assessment of stereoscopic 3D video

Banitalebi-Dehkordi

Nasiopoulos

2018

Multimed Tools Appl

View full text Add to dashboard Cite

To study the visual attentional behavior of Human Visual System (HVS) on 3D content, eye tracking experiments are performed and Visual Attention Models (VAMs) are designed. One of the main applications of these VAMs is in quality assessment of 3D video. The usage of 2D VAMs in designing 2D quality metrics is already well explored. This paper investigates the added value of incorporating 3D VAMs into Full-Reference (FR) and No-Reference (NR) quality assessment metrics for stereoscopic 3D video. To this end, state-of-the-art 3D VAMs are integrated to quality assessment pipeline of various existing FR and NR stereoscopic video quality metrics. Performance evaluations using a large scale database of stereoscopic videos with various types of distortions demonstrated that using saliency maps generally improves the performance of the quality assessment task for stereoscopic video. However, depending on the type of distortion, utilized metric, and VAM, the amount of improvement will change. 1Index Terms -stereoscopic video, 3D video, saliency prediction, quality assessment, visual attention modeling.

show abstract

“…In recent years, a series of different SVQA algorithms are proposed. B. Appina et al [1] utilized the frame categorization of a video sequence by using optical flow field strength to compute the dependencies, then computing quality by using depth features and spatio-temporal features. Hong et al [2] used 3-D perceptual quality index (3-D-PQI) to measure video compression distortion of stereoscopic videos.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature-Guided Stereoscopic Video Quality Assessment Based on 3d Convolutional Neural Network

Feng

Chang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

With the huge development of stereoscopic video technology, the research of stereoscopic video quality assessment (SVQA) has become very important for promoting the development of stereoscopic video system. These years, many SVQA methods based on convolutional neural network (CNN) have emerged. In this paper, we proposed a multi-scale feature-guided 3D convolutional neural network for SVQA which not only use 3D convolution to capture spatio-temporal features but also aggregate multi-scale information by a new multi-scale unit. Besides, we employ a multi-stage growing attention mechanism in this network to learn more critical deep semantic information. The proposed method is tested on two public stereoscopic video quality datasets, and the result shows that this method correlates highly with human visual perception and outperforms stateof-the-art methods by a large margin.

show abstract

A full reference stereoscopic video quality assessment metric

Cited by 14 publications

References 34 publications

On the practical applications of objective quality metrics for stereoscopic 3D imaging

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Saliency inspired quality assessment of stereoscopic 3D video

Multi-Scale Feature-Guided Stereoscopic Video Quality Assessment Based on 3d Convolutional Neural Network

Contact Info

Product

Resources

About