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Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review
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Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
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Design of Effective Smart Communication System for Impaired People
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Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
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Prediction of Energy Consumption by Ships at the port using Deep Learning
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A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
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Power Transfer Capability Recognition in Deregulated System under Line Outage Condition Using Power World Simulator
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Transformer Oil Diagnostic Tests Analysis using Statistical Correlation Technique
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Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model
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Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
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Performance Analysis of Multiple Pico Hydro Power Generation
Volume-2 | Issue-2
Energy Efficient Data Mining Approach for Estimating the Diabetes
Volume-3 | Issue-2
Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
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A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Volume - 2 | Issue - 4 | december 2020
Published
23 February, 2021
There have been many compression standards developed during the past few decades and technological advances has resulted in introducing many methodologies with promising results. As far as PSNR metric is concerned, there is a performance gap between reigning compression standards and learned compression algorithms. Based on research, we experimented using an accurate entropy model on the learned compression algorithms to determine the rate-distortion performance. In this paper, discretized Gaussian Mixture likelihood is proposed to determine the latent code parameters in order to attain a more flexible and accurate model of entropy. Moreover, we have also enhanced the performance of the work by introducing recent attention modules in the network architecture. Simulation results indicate that when compared with the previously existing techniques using high-resolution and Kodak datasets, the proposed work achieves a higher rate of performance. When MS-SSIM is used for optimization, our work generates a more visually pleasant image.
KeywordsPSNR discretized Gaussian Mixture rate-distortion performance image compression network architecture
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