Perceptual hash algorithm‐based adaptive GOP selection algorithm for distributed compressive video sensing

Chen, Can; Ding, Fei; Zhang, Dengyin

doi:10.1049/iet-ipr.2017.0354

Cited by 16 publications

(18 citation statements)

References 24 publications

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“…The main framework of DCS includes independently compression of each signal and then, jointly reconstruction of multiple sparse signals. DCS has been successfully employed in many applications such as wireless sensor networks (WSNs) [15,16], video coding [17,18], image fusion [19,20], multiple-input multiple-output (MIMO) channel [21], multichannel electroencephalogram (EEG) [22], joint channel estimation [23], ground moving target indication (GMTI) [24], and multi-channel SAR system [25]. Three joint sparsity models (JSMs) are presented in [13]: JSM-1, JSM-2, and JSM-3 model.…”

Section: Introductionmentioning

confidence: 99%

Model-based decentralized Bayesian algorithm for distributed compressed sensing

Torkamani

Zayyani

Sadeghzadeh

2021

Signal Processing: Image Communication

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Section: Introductionmentioning

confidence: 99%

Model-based decentralized Bayesian algorithm for distributed compressed sensing

Torkamani

Zayyani

Sadeghzadeh

2021

Signal Processing: Image Communication

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“…In the literature, researchers have proposed diverse video hashing algorithms for many applications [13][14][15][16], such as copy detection, digital watermarking, tampering detection, content authentication and video retrieval. Some typical algorithms related to our used techniques are introduced below.…”

Section: Introductionmentioning

confidence: 99%

Efficient video hashing based on low‐rank frames

Chen

Tang

Zhang

et al. 2021

IET Image Processing

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Video hashing is a useful technology for diverse video applications, such as digital watermarking, copy detection and content authentication. This paper proposes a novel efficient video hashing based on low-rank frames. A key contribution is the low-rank frame calculation using the low-rank approximation of singular value decomposition (SVD). As the large singular values of SVD are stable to digital operations, video hash extraction using low-rank frames can provide good robustness. Since most energy is contained within the large singular values, low-rank frames also contribute to discrimination. Moreover, two-dimensional discrete wavelet transform (DWT) is applied to every low-rank frame and the mean of low-frequency DWT coefficients is selected as a hash element. Since these coefficients can represent input data approximately, hash discrimination is thus ensured. Experiments with 16,850 videos are carried out to test performances of the proposed algorithm. The results show that the proposed algorithm outperforms some well-known video hashing algorithms in computational time and classification about discrimination and robustness.

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“…Zhao added PTAM nonlinear optimization to solve the bit attitude, while Artal proposed the ORB SLAM algorithm for multicamera conditions [12]. Chen et al demonstrated the requirement for consistency of luminance value characteristics on ambient illumination by photographing the same target under identical conditions except for luminance, but the results of the photographing made it difficult to determine whether the objects in the picture were the same target [13]. e luminosity of the target is high.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Motion Estimation Based on the Interframe Difference Detection Function Model

Zhang

Kang

2021

Complexity

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In this paper, we simulate the estimation of motion through an interframe difference detection function model and investigate the spatial-temporal context information correlation filtering target tracking algorithm, which is complex and computationally intensive. The basic theory of spatiotemporal context information and correlation filtering is studied to construct a fast target tracking method. The different computational schemes are designed for the flow of multiframe target detection from background removal to noise reduction, to single-frame detection, and finally to multiframe detection, respectively. This enables the ground-based telescope to effectively detect spatial targets in dense stellar backgrounds in both modes. The method is validated by simulations and experiments and can meet the requirements of real projects. The interframe bit attitude estimation is optimized by using the beam-parity method to reduce the interframe estimation noise; a global optimization strategy based on the bit attitude map is used in the back end to reduce the system computation amount and make the global bit attitude estimation more accurate; a loop detection based on the word pocket model is added to the system to reduce the cumulative error.

show abstract

Perceptual hash algorithm‐based adaptive GOP selection algorithm for distributed compressive video sensing

Cited by 16 publications

References 24 publications

Model-based decentralized Bayesian algorithm for distributed compressed sensing

Model-based decentralized Bayesian algorithm for distributed compressed sensing

Efficient video hashing based on low‐rank frames

An Algorithm for Motion Estimation Based on the Interframe Difference Detection Function Model

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