Image quality assessment based on DCT subband similarity

Balanov, Amnon; Schwartz, Arik; Moshe, Yair; Peleg, Nimrod

doi:10.1109/icip.2015.7351172

Cited by 38 publications

(31 citation statements)

References 21 publications

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“…It is significantly indispensable for the IQA task to convert spatial domain of an image into transformed domain to extract more useful features. The model in [39] calculates the correlation among each sub-band in the discrete cosine transform (DCT) domain and then applies the Gaussian weighted function for each sub-band to simulate the important functions of HVS. It is true that most of the image encoders tend to deal with each spatial frequency separately, thus leading to a wide range of distortion intensity in different frequency sub-bands, so all distortion information can be captured efficiently by calculating the similarity of each DCT sub-band separately.…”

Section: Extraction Of Features In the Transformed Domainmentioning

confidence: 99%

Image quality assessment via spatial‐transformed domains multi‐feature fusion

Yao

Liao

et al. 2020

IET Image Processing

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Section: Extraction Of Features In the Transformed Domainmentioning

confidence: 99%

Image quality assessment via spatial‐transformed domains multi‐feature fusion

Yao

Liao

et al. 2020

IET Image Processing

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“…During the final pooling, the saliency value of each pixel is used as a weighting value which affects the importance of different pixels. The DCT sub-bands similarity (DSS) [23] index uses changes in the structural information of the subbands in the DCT domain and the weighted quality estimates for these subbands to predict image quality. Visual information fidelity (VIF) [24] criterion was proposed to quantify the loss of image information to the distortion process and explore the relationship between image information and visual quality.…”

Section: A Fr-iqamentioning

confidence: 99%

“…Content may change prior to final publication. In this paper, we select fifteen IQA metrics to compute features: SSIM [2], PSNR [1], PSNR-HVS [17], MAD [15], PSNR-HMA [18], FSIM [21], FSIMc [21], ESSIM [19], VSI [22], DSS [23], OSS-SSIM [20],VIF [24],GMSD [25], iCID [26], and MDSI [27]. All metrics have positive correlation with subjective visual perception except for the MAD, GMS-D, iCID, and MDSI metrics.…”

Section: A Feature Extraction 1) Existing Iqa Metricsmentioning

confidence: 99%

Pairwise Learning to Rank for Image Quality Assessment

et al. 2020

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Because the pairwise comparison is a natural and effective way to obtain subjective image quality scores, we propose an objective full-reference image quality assessment (FR-IQA) index based on pairwise learning to rank (PLR). We first compose a large number of pairs of images, extract their features, and compute their preference labels as training labels. We then obtain a pairwise preference model by training a binary classifier using the features and labels. Because image quality is affected by the masking effect, we propose extracting frequency-aware quality features by adapting state-of-the-art IQA metrics. The learned pairwise preference model is then used to predict the preference between pairs of images in the testing dataset. The quality of each image is computed as the number of preferences. Experimental results on four IQA databases validate that the proposed PLR-based IQA index achieves higher consistency with human subjective evaluation than the state-of-the-art IQA metrics.

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“…During the final pooling, the saliency value at each pixel is used as a weighting value, which affects the importance of different pixels. DCT subbands similarity (DSS) [32] index uses changes in the structural information of the subbands in the DCT domain and the weighted quality estimates for these subbands to predict image quality. Nafchi et al [33] proposed a mean deviation similarity index (MDSI) to assess image quality by considering gradient similarity and chromaticity similarity to measure structural and color distortions, respectively.…”

Section: ) Full-reference 2d Image Fidelity Assessmentmentioning

confidence: 99%

“…Early content-aware and HVS-based FR metrics [24], [26], [27], [32]- [34] have achieved good results on the LIVE and CSIQ databases. After that, the multi-strategy-based FR metrics [38], [39] have further improved the performance, especially on the TID2008 database.…”

Section: ) Tid2013 Database [123]mentioning

confidence: 99%

2D and 3D Image Quality Assessment: A Survey of Metrics and Challenges

et al. 2019

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Image quality is important not only for the viewing experience, but also for the performance of image processing algorithms. Image quality assessment (IQA) has been a topic of intense research in the fields of image processing and computer vision. In this paper, we first analyze the factors that affect twodimensional (2D) and three-dimensional (3D) image quality, and then provide an up-to-date overview on IQA for each main factor. The main factors that affect 2D image quality are fidelity and aesthetics. Another main factor that affects stereoscopic 3D image quality is visual comfort. We also describe the IQA databases and give the experimental results on representative IQA metrics. Finally, we discuss the challenges for IQA, including the influence of different factors on each other, the performance of IQA metrics in real applications, and the combination of quality assessment, restoration, and enhancement. INDEX TERMS Image quality assessment, image aesthetics assessment, visual comfort, and image quality enhancement.

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Image quality assessment based on DCT subband similarity

Cited by 38 publications

References 21 publications

Image quality assessment via spatial‐transformed domains multi‐feature fusion

Image quality assessment via spatial‐transformed domains multi‐feature fusion

Pairwise Learning to Rank for Image Quality Assessment

2D and 3D Image Quality Assessment: A Survey of Metrics and Challenges

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