Background Error Propagation Model Based RDO in HEVC for Surveillance and Conference Video Coding

Xiong, Jian; Long, Xianzhong; Shi, Ran; Wang, Miaohui; Yang, Jie; Gui, Guan

doi:10.1109/access.2018.2879329

Cited by 18 publications

(9 citation statements)

References 28 publications

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“…We have proven that our network can be generalized in diverse types of blur kernels even if it is large and obtain good results with high levels of noise pollution. In the future work, solving the over-smoothing problem while maintaining high anti-noise performance [32]- [37] will be the focus of our work.…”

Section: Discussionmentioning

confidence: 99%

Non-Blind Image Deblurring Method by the Total Variation Deep Network

et al. 2019

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There are a lot of non-blind image deblurring methods, especially with the total variation (TV) model-based method. However, how to choose the parameters adaptively for regularization is a major open problem. We proposed a very novel method that is based on the TV deep network to learn the best parameters adaptively for regularization. We used deep learning and prior knowledge to set up a TV-based deep network and calculate the parameters of regularization, such as biases and weights. Therefore, we used the idea of a deep network to update these parameters automatically to avoid sophisticated calculations. Our experimental results by our proposed network are significantly better than several other methods, in respect of detail retention and anti-noise performance. At the same time, we can achieve the same effect with a minimum number of training sets, thus speeding up the calculation. INDEX TERMS Non-blind image deblurring, total variation model, deep learning.

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Section: Discussionmentioning

confidence: 99%

Non-Blind Image Deblurring Method by the Total Variation Deep Network

et al. 2019

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“…The first stream processes the information from foreground which is used to evaluate its completeness and consistence. The second stream models the information from background [12] which is designed to assess the difference between foreground and background when combined with the first stream.…”

Section: Image Content Informationmentioning

confidence: 99%

Multi Information Fusion Network for Saliency Quality Assessment

Tan

Meng

et al. 2019

IEICE Trans. Inf. & Syst.

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Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth. Existing works typically evaluate saliency quality by utilizing information from saliency maps to assess its compactness and closedness while ignoring the information from image content which can be used to assess the consistence and completeness of foreground. In this letter, we propose a novel multi-information fusion network to capture the information from both the saliency map and image content. The key idea is to introduce a siamese module to collect information from foreground and background, aiming to assess the consistence and completeness of foreground and the difference between foreground and background. Experiments demonstrate that by incorporating image content information, the performance of the proposed method is significantly boosted. Furthermore, we validate our method on two applications: saliency detection and segmentation. Our method is utilized to choose optimal saliency map from a set of candidate saliency maps, and the selected saliency map is feeded into an segmentation algorithm to generate a segmentation map. Experimental results verify the effectiveness of our method.

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“…Yin et al [13] traced distortion propagation with ρ-domain distortion model [14] and solved the optimal QP by trellis. Xiong et al [15] used the linear distortion model [9] to analyse the impact of distortion of long term frames on distortion of other frames and then solved the optimal QP for each long term frame. There are also some studies in which inter-frame dependency is not directly quantified but implied when solving optimal QP or λ.…”

Section: Introductionmentioning

confidence: 99%

Sub-Frame Layer Rate Distortion Optimization for High Efficiency Video Coding

Wei

Zhou

et al. 2020

IEEE Access

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This paper proposes a sub-frame layer RDO algorithm to further improve coding performance of low-delay hierarchical prediction structure in High Efficiency Video Coding. In the proposed algorithm, each frame is segmented into two sub-frames according to motion intensity, and a sub-frame layer is taken as a basic optimizing unit. An inter-frame dependency model is proposed to quantify inter-frame dependency of a frame or a sub-frame. The proposed inter-frame dependency model is composed of two parts, Laplace distribution based direct inter-frame dependency model and distortion propagation chain. The direct inter-frame dependency model is used to evaluate dependency caused by direct reference, and the distortion propagation chain shows the relationship between direct reference and indirect reference. With quantified inter-frame dependency, Lagrange optimization is then used to solve the optimal λ-QP pair for each sub-frame layer in frame layer 1 (L1) to frame layer 4 (L4). For frame layer 0 (L0), additional L0 frames are adaptively inserted according to inter-frame dependency of the previous L0 frame, and the optimal λ-QP pair for each sub-frame in L0 is solved using Lagrange optimization. When peak signal to noise ratio is used as quality criterion, the proposed algorithm achieves 7.0% and 7.0% bit-rate reduction with lowdelay_P_main (LDP) and lowdelay_main (LDB) configurations respectively; when structural similarity is used as quality criterion, the proposed algorithm achieves 13.3% and 13.1% bit-rate reduction with LDP and LDB configurations respectively. INDEX TERMS High efficiency video coding, hierarchical prediction structure, rate distortion optimization, sub-frame layer.

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Background Error Propagation Model Based RDO in HEVC for Surveillance and Conference Video Coding

Cited by 18 publications

References 28 publications

Non-Blind Image Deblurring Method by the Total Variation Deep Network

Non-Blind Image Deblurring Method by the Total Variation Deep Network

Multi Information Fusion Network for Saliency Quality Assessment

Sub-Frame Layer Rate Distortion Optimization for High Efficiency Video Coding

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