Hierarchical Predictive Coding-Based JND Estimation for Image Compression

Wang, Hongkui; Yu, Li; Liang, Junhui; Yin, Haibing; Li, Tiansong; Wang, Sheng-Wei

doi:10.1109/tip.2020.3037525

Cited by 25 publications

(11 citation statements)

References 42 publications

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“…Perceptual-based video coding that explores perceptual redundancy is a promising research area worthy of future consideration [148]- [150]. Related research aims to realize an optimal RC mechanism by constructing the relationship between video quality assessment (VQA) and the coding rate.…”

Section: Perceptual and Depth-aware Rc Methodsmentioning

confidence: 99%

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

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Video coding is a video compression technique that compresses the original video sequence to achieve a smaller archive file or a lower transmission bandwidth under constraints on the visual quality loss. Rate control (RC) plays a critical role in video coding. It can achieve stable stream output in practical applications, especially in real-time video applications such as video conferencing or game live streaming. Most RC algorithms directly or indirectly characterize the relationship between the bit rate (R) and quantization (Q) and then allocate bits for every coding unit to guarantee the global bit rate and video quality level. This paper comprehensively reviews the classic RC technologies used in international video standards of past generations, analyses the mathematical models and implementation mechanisms of various schemes, and compares the performance of recent state-of-the-art RC algorithms. Finally, we discuss future directions and new application areas for RC methods. We hope that this review can help support the development, implementation, and application of RC in new video coding standards.

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Section: Perceptual and Depth-aware Rc Methodsmentioning

confidence: 99%

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The perceptual effects induced by positive stimuli are known to be positive perceptual effects [ 46 ]. Moreover, the transmission and expression of visual information requires energy consumption [ 47 ].…”

Section: Analysis Of Color Feature Parametersmentioning

confidence: 99%

Pixel-Domain Just Noticeable Difference Modeling with Heterogeneous Color Features

Yin

Wang

et al. 2023

Sensors

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With the rapidly emerging user-generated images, perception compression for color image is an inevitable mission. Whilst in existing just noticeable difference (JND) models, color-oriented features are not fully taken into account for coinciding with HVS perception characteristics, such as sensitivity, attention, and masking. To fully imitate the color perception process, we extract color-related feature parameters as local features, including color edge intensity and color complexity, as well as region-wise features, including color area proportion, color distribution position and color distribution dispersion, and inherent feature irrelevant to color content called color perception difference. Then, the potential interaction among them is analyzed and modeled as color contrast intensity. To utilize them, color uncertainty and color saliency are envisaged to emanate from feature integration in the information communication framework. Finally, color and uncertainty saliency models are applied to improve the conventional JND model, taking the masking and attention effect into consideration. Subjective and objective experiments validate the effectiveness of the proposed model, delivering superior noise concealment capacity compared with start-of-the-art works.

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“…Usually, the visual acuity decreases with the distance between the pixel and the fixation point. This visual characteristic is FIGURE 10 Temporal saliency map of the 67 th frame (AB-frame) in 'WebBrowsing' FIGURE 11 Predicted fixation points for AB-frames FIGURE 12 The retinal eccentricity described by the foveated weighting model, and it is directly related to the retinal eccentricity. As shown in Figure 12, r is the distance between the pixel z and the fixation point.…”

Section: Foveated Pr Model For Screen Content Videosmentioning

confidence: 99%

“…In general, existing PR models can be divided into two main categories, that is, pixel-based PR models and subband-based PR models. The pixel-based PR model is built in the pixel domain, which is applied to estimate the PR threshold of each pixel [6][7][8][9][10][11][12][13]. In terms of the subband-based PR model, it is usually constructed in the frequency domain (e.g., DCT and DWT) to estimate the perceptual redundancy for each transform coefficient [14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Perceptual redundancy model for compression of screen content videos

Wang

2022

IET Image Processing

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Screen content video (SCV) consists primarily of text areas, computer graphics and other computer-generated content and possesses unique perceptual characteristics. To compress SCVs more effectively with less reduction in subjective quality, perceptual characteristics of SCVs are analyzed and a perceptual redundancy (PR) model for SCV compression is proposed, including spatial PR (SPR), temporal PR (TPR) and foveated PR (FPR) model. In SPR modeling, the SCV is divided into sharp edge (SE) areas and non-SE areas, then SPR is estimated separately. In TPR modeling, both inter-frame luminance adaptation effect and motion masking effect are taken into account. In FPR modeling, each frame of SCV is classified into abrupt frames, relative motion frames or static frames. Then fixation points of different kinds of frames are predicted using different methods, and FPR is modeled considering foveated masking effect and visual attention. Finally, the perceptual redundancy of SCV is estimated based on the product of SPR, TPR and FPR. It is experimentally demonstrated that compared to the state-of-the-art models, the authors' model could obtain more accurate estimates of PR. Moreover, the model is incorporated into SCV compression with an adaptive perceptual quantizer. An average of 7.42% bits could be saved with less decline in subjective quality.

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Hierarchical Predictive Coding-Based JND Estimation for Image Compression

Cited by 25 publications

References 42 publications

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Pixel-Domain Just Noticeable Difference Modeling with Heterogeneous Color Features

Perceptual redundancy model for compression of screen content videos

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