Subjective quality evaluation of compressed digital compound images

Yang, Huan; Fang, Yuming; Yuan, Yuan; Wang, Lin

doi:10.1016/j.jvcir.2014.11.001

Cited by 9 publications

(6 citation statements)

References 41 publications

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“…The blur metric scores for SCIs and natural images are shown in Table I. The SCIs are obtained from the database [25]. It is observed that the SCIs exhibit sharper appearance because of larger values of S, which verifies that the SCIs usually contain more thinner edges compared with natural images.…”

Section: B Sharpness Of Edgesmentioning

confidence: 97%

“…As illustrated in Fig. 7, 10 sample SCIs from database [25] are used in the testing. The screen resolution is 1920x1080 and the viewing distance is about two times of the screen height.…”

Section: B Luminance Adaptationmentioning

confidence: 99%

“…13, fifteen SCIs from both the screen content quality assessment database [25] and HEVC range extension test sequences are used for testing. It is noted that to achieve cross-validation, the SCIs in this experiments are different from the SCIs used in the subjective testing of Section III.…”

Section: Descriptionmentioning

confidence: 99%

“…Moreover, in [23] it is observed that the extent of the visual field used to extract information in SCIs is much smaller than that of natural images. In view of these distinct features of SCIs, in [24], [25] a database with subjective quality ranking of distorted SCIs was created. It demonstrates that the state-of-the-art perceptual models are still quite limited in predicting the visual quality of the SCIs, and inspires a series of specifically developed full-reference SCI quality assessment measures recently [26], [27].…”

mentioning

confidence: 99%

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Just Noticeable Difference Estimation for Screen Content Images

Wang

Fang

et al. 2016

IEEE Trans. on Image Process.

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We propose a novel just noticeable difference (JND) model for a screen content image (SCI). The distinct properties of the SCI result in different behaviors of the human visual system when viewing the textual content, which motivate us to employ a local parametric edge model with an adaptive representation of the edge profile in JND modeling. In particular, we decompose each edge profile into its luminance, contrast, and structure, and then evaluate the visibility threshold in different ways. The edge luminance adaptation, contrast masking, and structural distortion sensitivity are studied in subjective experiments, and the final JND model is established based on the edge profile reconstruction with tolerable variations. Extensive experiments are conducted to verify the proposed JND model, which confirm that it is accurate in predicting the JND profile, and outperforms the state-of-the-art schemes in terms of the distortion masking ability. Furthermore, we explore the applicability of the proposed JND model in the scenario of perceptually lossless SCI compression, and experimental results show that the proposed scheme can outperform the conventional JND guided compression schemes by providing better visual quality at the same coding bits.

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Section: B Sharpness Of Edgesmentioning

confidence: 97%

“…As illustrated in Fig. 7, 10 sample SCIs from database [25] are used in the testing. The screen resolution is 1920x1080 and the viewing distance is about two times of the screen height.…”

Section: B Luminance Adaptationmentioning

confidence: 99%

Section: Descriptionmentioning

confidence: 99%

mentioning

confidence: 99%

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Just Noticeable Difference Estimation for Screen Content Images

Wang

Fang

et al. 2016

IEEE Trans. on Image Process.

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“…Web pages, PowerPoint and PDF files and computer screen images) are represented in the form of digital compound images (DCIs). For efficient transmission among different devices, transmission systems should be deployed in a platform-free manners (without consideration of different protocols of different systems) and DCIs have to be highly compressed [5]. To improve the coding efficiency over the text and graphics regions, some approaches have been proposed in the past decade.…”

Section: Introductionmentioning

confidence: 99%