Detection and Localization of Video Transcoding From AVC to HEVC Based on Deep Representations of Decoded Frames and PU Maps

Yao, Haichao; Ni, Rongrong; Amirpour, Hadi; Timmerer, Christian; Zhao, Yao

doi:10.1109/tmm.2022.3185890

Cited by 5 publications

(1 citation statement)

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“…The recommended method produces clearer images than baseline techniques, increasing PSNR by 1.3 dB. One potential method for finding exact motion vectors with minimal processing complexity was the Multi-Directional Motion assessment [32]. The Adaptive Motion Vector Smoothing Algorithm was proposed in the MVS phase as a different name for the AGMVS algorithm [33] in a way that modifies the motion vectors' failure by particular values in the in the unified framework.…”

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

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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.

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Section: Introductionmentioning

confidence: 99%