Just noticeable distortion model and its applications in video coding

Yang, Xiaokang; Wang, Lin; Lu, Zhongkang; Ong, Ee Ping; Yao, Susu

doi:10.1016/j.image.2005.04.001

Cited by 276 publications

(248 citation statements)

References 25 publications

(42 reference statements)

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“…The component related to intensity contrast is based on the higher sensitivity of the HVS to luminance variations with respect to the environment of a stimulus, than to the absolute luminance of the stimulus, as stated by the Weber-Fechner law. In fact, a piecewise approximation of the sensitivity threshold was used [6], Moreover, the temporal masking factor is modeled as a function of the luminance difference between corresponding MB of contiguous frames [5]. Finally, the threshold related to the local activity of the environment of the analyzed MB is computed from the difference between the maximum and the minimum mean luminance of the neighboring MBs [6].…”

Section: Sbs-format-aware Perceptual Classificationmentioning

confidence: 99%

“…Especially for stereoscopic video, the loss of different frames cause dissimilar levels of binocular rivalry and visual discomfort [4], Thus, we study the use of perceptual features to derive the distortion model with the aim of maximizing the end user QoE. These features are based on a Just Noticeable Distortion (HMD) model, since they can provide information about the maximum distortion that the signal can suffer without being perceived [5]. These models have been extensively used in the field of audio and video compression and also applied to quality metrics, thanks to their direct relation to the perceptual behavior of the human perception [6],…”

Section: Introductionmentioning

confidence: 99%

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Perceptual distortion modeling for side-by-side 3D video delivery

Díaz

Gutiérrez

Cabrera

et al. 2014

2014 IEEE International Conference on Consumer Electronics (ICCE)

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Section: Sbs-format-aware Perceptual Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Perceptual distortion modeling for side-by-side 3D video delivery

Díaz

Gutiérrez

Cabrera

et al. 2014

2014 IEEE International Conference on Consumer Electronics (ICCE)

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“…The subjective viewing test used in [22][23][24] is performed to accomplish the quality assessment and determine the parameters in Equations (5) and (6) by comparing the original color image and its noise-contaminated color image that is given by randomly adding to or subtracting from each the wavelet coefficient by its estimated JND value. The thumbnail of the test color image set is shown in Figure 2.…”

Section: Visual Maskingmentioning

confidence: 99%

Prediction error preprocessing for perceptual color image compression

Liu

2012

J Image Video Proc

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In this article, a prediction error preprocessor based on the just noticeable distortion (JND) for the color image compression scheme is presented. The dynamic range of prediction error signals we can reduce, the lower bit rate of the reconstructed image we can obtain at high visual quality. We propose a color JND estimator that is incorporated into the design of the preprocessor in the compression scheme. The color JND estimator is carried out in the wavelet domain to present good estimates to the available amount masking. The estimated JND is used to preprocess the signal and is also used to incorporate into the design of the quantization stage in the compression scheme for higher performance. Simulation results show that the bit rate required by the compression scheme with the preprocessor is lower at high visual quality of the reconstructed color image. The preprocessor is further applied to the input color image of the JPEG and JPEG2000 coders for better performance.

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“…For example, UQI [12] and SSIM [3] are constructed based on the conclusion that human vision is more sensitive to the degradation on structural information of an image, such as the distortions in edge regions. Due to the fact that human eyes cannot perceive all distortions, PHVS [2] and JND [13] models are established based on the CSF and masking effect of the HVS from the Discrete Cosine Transform (DCT) domain and pixel domain, respectively.…”

Section: Visual Attention Modelsmentioning

confidence: 99%

Improving image quality assessment with modeling visual attention

You

Perkis

Gabbouj

2010

2010 2nd European Workshop on Visual Information Processing (EUVIP)

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Visual attention is an important attribute of the human visual system (HVS), while it has not been explored in image quality assessment adequately. This paper investigates the capabilities of visual attention models for image quality assessment in different scenarios: twodimensional images, stereoscopic images, and Digital Cinema setup. Three bottom-up attention models are employed to detect attention regions and find fixation points from an image and compute respective attention maps. Different approaches for integrating the visual attention models into several image quality metrics are evaluated with respect to three different image quality data sets. Experimental results demonstrate that visual attention is a positive factor that can not be ignored in improving the performance of image quality metrics in perceptual quality assessment.

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Just noticeable distortion model and its applications in video coding

Cited by 276 publications

References 25 publications

Perceptual distortion modeling for side-by-side 3D video delivery

Perceptual distortion modeling for side-by-side 3D video delivery

Prediction error preprocessing for perceptual color image compression

Improving image quality assessment with modeling visual attention

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