A ParaBoost stereoscopic image quality assessment (PBSIQA) system

Ko, Hyunsuk; Song, Rui; Kuo, C.-C. Jay

doi:10.1016/j.jvcir.2017.02.014

Cited by 12 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early NR-SIQA methods only focused on the statistical or structural characteristics of images without considering the human binocular mechanism [3,4]. For example, Mittal et al [3] extracted the natural scene statistics features of distorted images, and feed them into the support vector regression (SVR) to calculate the quality score.…”

Section: Introductionmentioning

confidence: 99%

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Shen,

Li,

Pan

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Human beings perceive stereoscopic image quality through the cerebral visual cortex, which is a complex brain activity. As a solution, the quality of stereoscopic images can be evaluated more accurately by attempting to replicate the human perception from electroencephalogram (EEG) signals on image quality in a machine, which is different from previous stereoscopic image quality assessment methods focused only on the extraction of image features. Approach. Our proposed method is based on a novel image-to-brain (I2B) cross-modality model including a spatial-temporal EEG encoder (STEE) and an I2B deep convolutional generative adversarial network (I2B-DCGAN). Specifically, the EEG representations are first learned by STEE as real samples of I2B-DCGAN, which is designed to extract both quality and semantic features from the stereoscopic images by a semantic-guided image encoder, and utilize a generator to conditionally create the corresponding EEG features for images. Finally, the generated EEG features are classified to predict the image perceptual quality level. Main results. Extensive experimental results on the collected brain-visual multimodal stereoscopic image quality ranking database, demonstrate that the proposed I2B cross-modality model can better emulate the visual perception mechanism of the human brain and outperform the other methods by achieving an average accuracy of 95.95 % . Significance. The proposed method can convert the learned stereoscopic image features into brain representations without EEG signals during testing. Further experiments verify that the proposed method has good generalization ability on new datasets and the potential for practical applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Shen,

Li,

Pan

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, existing theory indicates that none of the existing IQA algorithms can achieve the best results in all cases. Therefore, the new score is set as a nonlinear combination of quality scores via multiple metrics and appropriate weights acquired through the training process [4]- [6]. For instance, different quality evaluation approaches can deal with different types of image distortions well.…”

Section: Introductionmentioning

confidence: 99%

No Reference Quality Assessment for Screen Content Images Using Stacked Autoencoders in Pictorial and Textual Regions

Yang

Zhao

Liu

et al. 2022

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Recently, the visual quality evaluation of screen content images (SCIs) has become an important and timely emerging research theme. This paper presents an effective and novel blind quality evaluation metric for SCIs by using stacked auto-encoders (SAE) based on pictorial and textual regions. Since the SCI consists of not only the pictorial area but also the textual area, the human visual system (HVS) is not equally sensitive to their different distortion types. Firstly, the textual and pictorial regions can be obtained by dividing an input SCI via a SCI segmentation metric. Next, we extract quality-aware features from the textual region and pictorial region, respectively. Then, two different SAEs are trained via an unsupervised approach for quality-aware features which are extracted from these two regions. After the training procedure of the SAEs, the quality-aware features can evolve into more discriminative and meaningful features. Subsequently, the evolved features and their corresponding subjective scores are input into two regressors for training. Each regressor can obtain one output predictive score. Finally, the final perceptual quality score of a test SCI is computed by these two predicted scores via a weighted model. Experimental results on two public SCI-oriented databases have revealed that the proposed scheme can compare favorably with the existing blind image quality assessment metrics.Index Terms-Screen content image (SCI), human visual system (HVS), stacked auto-encoders (SAE), quality-aware features, unsupervised approach.

show abstract

“…The scores of individual quality estimators are fused with a support vector regression stage along with a statistical testing-based selection mechanism. A similar parallel boosting approach based on support vector regression is also used for stereoscopic image quality assessment in [21].…”

Section: Introductionmentioning

confidence: 99%

Boosting in image quality assessment

Temel

AlRegib

2016

2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)

View full text Add to dashboard Cite

In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. Existing multi-method studies focus on proposing a single quality estimator. On the contrary, we investigate the generalizability of multimethod fusion as a framework. In addition to support vector machines that are commonly used in the multi-method fusion, we propose using neural networks in the boosting. To span different types of image quality assessment algorithms, we use quality estimators based on fidelity, perceptually-extended fidelity, structural similarity, spectral similarity, color, and learning. In the experiments, we perform k-fold cross validation using the LIVE, the multiply distorted LIVE, and the TID 2013 databases and the performance of image quality assessment algorithms are measured via accuracy-, linearity-, and ranking-based metrics. Based on the experiments, we show that boosting methods generally improve the performance of image quality assessment and the level of improvement depends on the type of the boosting algorithm. Our experimental results also indicate that boosting the worst performing quality estimator with two or more additional methods leads to statistically significant performance enhancements independent of the boosting technique and neural network-based boosting outperforms support vector machinebased boosting when two or more methods are fused.

show abstract

A ParaBoost stereoscopic image quality assessment (PBSIQA) system

Cited by 12 publications

References 47 publications

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

No Reference Quality Assessment for Screen Content Images Using Stacked Autoencoders in Pictorial and Textual Regions

Boosting in image quality assessment

Contact Info

Product

Resources

About