Video-Data Compression Using Wavelet Analysis

Yamnenko, Iuliia; Levchenko, Vitalii Viktorovych

doi:10.1109/cpee47179.2019.8949074

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Each trend and noise component includes approximation (Sj,k) and detailing (dj,k) coefficients. If we define the decomposition level (j) and consideration interval (k) as [22]:…”

Section: A Discrete Haar Wavelet Transformmentioning

confidence: 99%

Network-wide Traffic State Forecast Using Discrete Wavelet Transform and Deep Learning

Zada,

Yamnenko,

Antoniou

2023

2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Traffic state prediction models are a crucial element with many applications in intelligent transportation systems. Short-term network-wide modeling of traffic states is a challenging task due to the existence of inherent characteristics such as nonlinearity, periodicity and stochasticity in the traffic state time series. This issue was responded by the evolution of advanced machine learning algorithms, e.g. deep learning. Deep neural networks can cope with high dimensionality, and also, are capable of extracting nonlinearity, comovement patterns, and spatiotemporal interdependencies between the traffic state variables from different locations. Nevertheless, they cannot completely capture the location-specific features of traffic information. Therefore, we propose the Discrete Haar Wavelet Transform (DHWT) as a preprocessing scheme prior to Multilayer Perceptron (MLP) neural networks for one-hour ahead traffic state prediction. DHWT can help MLP to simultaneously learn the network-wide comovement patterns through the trend component time series, and seize the significant characteristics of each unique detector efficiently via the noise component. The results on 20 sensors in Paris indicated that the hybrid DHWT-MLP model with a two-level down decomposition improves the Mean Squared Error (MSE) of a non-preprocessed MLP by 33.73% and 17.58 %, for the six-month and three-month data, respectively. However, the proposed model does not perform well over the one-month period compared to the MLP model.Therefore, it may be helpful to use lower wavelet decomposition levels (higher orders) when dealing with relatively small traffic datasets.

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“…Each trend and noise component includes approximation (Sj,k) and detailing (dj,k) coefficients. If we define the decomposition level (j) and consideration interval (k) as [22]:…”

Section: A Discrete Haar Wavelet Transformmentioning

confidence: 99%

Network-wide Traffic State Forecast Using Discrete Wavelet Transform and Deep Learning

Zada,

Yamnenko,

Antoniou

2023

2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

Self Cite

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“…Results of the coding process indicate that the new encoder outperforms many state-of-the-art methods for still photo compression. In [15][16][17], the focus was based on the loss of video compression. Even at lower bit rates, the novel losscompression method improves contourlet compression performance.…”

Section: Related Workmentioning

confidence: 99%

“…If N 1 and N 2 signify the amounts of data holding units in the raw and encoded images, correspondingly, the compression ratio, CR, could be specified as CR � N 1 /N 2 as well as the data duplication of the original image as RD � 1 − (1/CR). From Table 3 and Figure 6, the proposed methodology can acquire the exact ratio of compression (10 : 1) when compared to Haar [17] (10 : 16.5) and Cosine [17] (10 : 17.2) techniques.…”

Section: Complexity Analysismentioning

confidence: 99%

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

He¹,

Gao²,

Zheng³

et al. 2021

wpt

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Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.

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Video-Data Compression Using Wavelet Analysis

Cited by 2 publications

References 6 publications

Network-wide Traffic State Forecast Using Discrete Wavelet Transform and Deep Learning

Network-wide Traffic State Forecast Using Discrete Wavelet Transform and Deep Learning

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

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