Just-noticeable difference estimation with pixels in images

Zhang, Xiao-Hui; Wang, Lin; Xue, Ping

doi:10.1016/j.jvcir.2007.06.001

Cited by 105 publications

(64 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Study on neural science has discovered that the primary and secondary visual cortexes (V1 and V2) are the earliest receptors for signal and play the most important part in generating vision [10]. Recent HVS-based methods try to incorporate just noticeable difference (JND) [11], contrast sensitivity function (CSF) [12], contrast masking [13], temporal masking, visual attention, and saliency map into the perceptual feature extraction. For example, the well-known method is visual information fidelity (VIF) [14] which incorporates the HVS model into information fidelity criterion (IFC) [15].…”

Section: Related Workmentioning

confidence: 99%

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Shang

Zhao

Ding

2018

Journal of Engineering

View full text Add to dashboard Cite

Image quality assessment that aims to evaluate the image quality automatically by a computational model plays a significant role in image processing systems. To meet the need of accuracy and effectiveness, in the proposed method, complementary features including histogram of oriented gradient, edge information, and color information are employed for joint representation of the image quality. Afterwards, the dissimilarities of the extracted features between the distorted and reference images are quantified. Finally, support vector regression is used for distortion indices fusion and objective quality mapping. Experimental results validate that the proposed method outperforms the state-of-the-art methods in terms of consistency with subjective perception and robustness across various databases and different distortion types.

show abstract

Section: Related Workmentioning

confidence: 99%

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Shang

Zhao

Ding

2018

Journal of Engineering

View full text Add to dashboard Cite

show abstract

“…where jnd is the index value calculated by JND model [5], g(jnd) is the corresponding score of subjective assessment grade given by the above empirical mapping function. Although the indicators of the auxiliary evaluation group are not consistent with the human's visual judgment, we still set up the empirical mapping function between grade quality evaluation and these objective assessments so that people can find the causes of impairment scales damaged by image modifications.…”

Section: The Design Of the Evaluation Systemmentioning

confidence: 99%

“…The perceptual adjustment based on Watson Just Noticeable Difference model can refer to the relevant literature [4]. Following this approach, an improved estimation of the JND in terms of luminance and contrast masking can be obtained literatures [5]. In [6], we built the corresponding relation between subjective evaluation criteria and objective assessment by JND model.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation System of the Image Quality with Subjective and Objective Uniformity

Niu¹,

Yang²

2018

dtcse

View full text Add to dashboard Cite

Abstract. Image quality evaluation standard mainly relied on the traditional standard like peak signal to noise ratio (PSNR), but PSNR does not meet people's subjective assessment. To obtain more accurate evaluation with subjective and objective uniformity, the multi-indexes are considered. Our evaluation system includes three parts: (1) grade evaluation; (2) auxiliary evaluation; (3) reference evaluation. The value of grade evaluation is mainly determined by the grade evaluation, and minor modifications are made by the auxiliary evaluation. The reference evaluation provides the traditional objective standards so as to comparative analysis. The results of actual operation show that it is comprehensive and effective.

show abstract

“…In this work, we incorporate the HVS properties into the contrast measure via the JND model [37][38] [39]. HVS is capable of only perceiving pixel intensity changes above a certain visibility threshold, which, in turn, is determined by the underlying physiological and psychophysical mechanisms.…”

Section: Energy Function For Region-based Image Segmentationmentioning

confidence: 99%

A human visual system-driven image segmentation algorithm

Peng¹,

Varshney²

2015

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Quality metrics for evaluating the segmentation result, from both region-based and boundary-based perspectives, are integrated into an objective function. The objective function encodes the HVS properties into a Markov random fields (MRF) framework, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation algorithms available in the literature. Results are very encouraging and show that the presented algorithms outperform the state-of-the-art image segmentation algorithms. Index Terms-Image segmentation, human visual system, Markov random fields, just-noticeable difference KEYWORDS: Abstract-This EDICS-ARS-RBS

show abstract

Just-noticeable difference estimation with pixels in images

Cited by 105 publications

References 24 publications

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

The Evaluation System of the Image Quality with Subjective and Objective Uniformity

A human visual system-driven image segmentation algorithm

Contact Info

Product

Resources

About