Fast Depth Intra Coding Based on Depth Edge Classification Network in 3D-HEVC

Liu, Chang; Jia, Kebin; Liu, Pengyu

doi:10.1109/tbc.2021.3106143

Cited by 17 publications

(6 citation statements)

References 35 publications

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“…By employing CNNs to fuse multi-domain references, they achieved significant bitrate savings compared to 3D-HEVC. Liu et al [22] proposed a CNN-based fast deep intra-frame coding to reduce the complexity of 3D-HEVC, which effectively reduced the intra-frame coding time while guaranteeing the coding performance.…”

Section: Deep Learning-based Mvcmentioning

confidence: 99%

Multi-View Video Coding with GAN Latent Learning

Lan¹,

Luo²,

Yan³

et al. 2022

Preprint

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The introduction of multiple viewpoints inevitably increases the bitrates to store and transmit video scenes. To reduce the compressed bitrates, researchers have developed to skip intermediate viewpoints during compression and delivery, and finally reconstruct them with Side Information (SI). Generally, the depth maps can be utilized to construct SI; however, it shows inferior performance with inaccurate reconstruction or high bitrates. In this paper, we propose a multi-view video coding based on SI of Generative Adversarial Network (GAN). At the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and further utilize convolutional network to extract the latent code of GAN as SI; while at the decoder side, we combine the SI and adjacent viewpoints to reconstruct intermediate views by the generator of GAN. In particular, we set a joint encoder constraint of reconstruction cost and SI entropy, in order to achieve an optimal tradeoff between reconstruction quality and bitrate overhead. Experiments show a significantly improved Rate-Distortion (RD) performance compared with the state-of-the-art methods.

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Section: Deep Learning-based Mvcmentioning

confidence: 99%

Multi-View Video Coding with GAN Latent Learning

Lan¹,

Luo²,

Yan³

et al. 2022

Preprint

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“…In this case, adding an initial search prediction is necessary. The search point in the first search step of our suggested technique is initially upgraded [13]. Here, the algorithm can locate the fundamental search point precisely and avoid the ineffective global search process.…”

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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“…From audio to images to video, multimedia technology has brought people increasingly strong sensory experiences. Traditional 2D videos can only give people a flat feeling, so in order to present more realistic effects, 3D videos are gradually entering our lives [1][2][3]. Compared with traditional single view videos, the data volume of multi view videos increases exponentially.…”

Section: Introductionmentioning

confidence: 99%

A fast 3D-HEVC video encoding algorithm based on Bayesian decision

Wang

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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Fast Depth Intra Coding Based on Depth Edge Classification Network in 3D-HEVC

Cited by 17 publications

References 35 publications

Multi-View Video Coding with GAN Latent Learning

Multi-View Video Coding with GAN Latent Learning

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

A fast 3D-HEVC video encoding algorithm based on Bayesian decision

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