Analysis of Deep Rain Streaks Removal Convolutional Neural Network-Based Post-Processing Techniques in HEVC Encoder

Jayaraman, Thiyagarajan; Chinnusamy, Gowri Shankar

doi:10.1142/s0218126621500201

Cited by 4 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the trial results, the recommended approach can encode video information with an average cost reduction of 44.92 percent and with the least amount of picture quality loss. The creators of [25]- [26] improved the HEVC's HM 16 tool was the reason for the process's good encoding efficiency and 1 % increase in the BD rate. Reference 27 outlines a method that streamlines the calculation and expedites reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Dommeti,

Dharani,

Shasidhar

et al. 2024

SCPE

View full text Add to dashboard Cite

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

show abstract

Section: Introductionmentioning

confidence: 99%

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Dommeti,

Dharani,

Shasidhar

et al. 2024

SCPE

View full text Add to dashboard Cite

show abstract

“…A deep rain streak removal CNN (DeRain SRCNN)-based deraining framework is proposed with two residual block layers and a dual channel rectification linear unit (DCReLU) (Jayaraman and Chinnusamy, 2020a;Kowal et al, 2021). This deraining network provides higher PSNR and SSIM values for both synthetic and real-time images compared with other existing deraining networks.…”

mentioning

confidence: 99%

Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network

Jayaraman,

Chinnusamy

2022

View full text Add to dashboard Cite

The visual appearance of outdoor captured images is affected by various weather conditions, such as rain patterns, haze, fog and snow. The rain pattern creates more degradation in the visual quality of the image due to its physical structure compared with other weather conditions. Also, the rain pattern affects both foreground and background image information. The removal of rain patterns from a single image is a critical process, and more attention is given to remove the structural rain pattern from real-time rain images. In this paper, we analyze the single image deraining problem and present a solution using the dual stage deep rain streak removal convolutional neural network. The proposed single image deraining framework primarily consists of three main blocks: a derain streaks removal CNN (derain SRCNN), a modified residual dense block (MRDB), and a six-stage scale feature aggregation module (3SFAM). The ablation study is conducted to evaluate the performance of various modules available in the proposed deraining network. The robustness of the proposed deraining network is evaluated over the popular synthetic and real-time data sets using four performance metrics such as the peak signal-to-noise ratio (PSNR), the feature similarity index (FSIM), the structural similarity index measure (SSIM), and the universal image quality index (UIQI). The experimental results show that the proposed framework outperforms both synthetic and real-time images compared with other state-of-the-art single image deraining approaches. In addition, the proposed network takes less running and training time.

show abstract

Comprehensive Experimental Evaluation of Open Source Deep Learning Framework for Single Image Deraining Applications

Jayaraman¹,

Manimaran²,

Senthilkumar

et al. 2023

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)

View full text Add to dashboard Cite

Analysis of Deep Rain Streaks Removal Convolutional Neural Network-Based Post-Processing Techniques in HEVC Encoder

Cited by 4 publications

References 25 publications

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network

Comprehensive Experimental Evaluation of Open Source Deep Learning Framework for Single Image Deraining Applications

Contact Info

Product

Resources

About