Improved HEVC video compression algorithm using low-complexity frame rate up conversion

2021 ◽  
Vol 30 (03) ◽  
Author(s):  
Hongwei Lin ◽  
Xiangqun Li ◽  
Mingliang Gao ◽  
Tao Li
Keyword(s):  
Video Compression ◽  
Low Complexity ◽  
Compression Algorithm ◽  
Frame Rate ◽  
Frame Rate Up Conversion

scholarly journals Efficient Video Frame Interpolation Using Generative Adversarial Networks

2020 ◽  
Vol 10 (18) ◽  
pp. 6245
Author(s):  
Quang Nhat Tran ◽  
Shih-Hsuan Yang
Keyword(s):  
Video Compression ◽  
State Of The Art ◽  
Motion Blur ◽  
The State ◽  
Frame Rate ◽  
Computational Time ◽  
Generative Adversarial Networks ◽  
Video Frame ◽  
Frame Interpolation ◽  
Frame Rate Up Conversion

Frame interpolation, which generates an intermediate frame given adjacent ones, finds various applications such as frame rate up-conversion, video compression, and video streaming. Instead of using complex network models and additional data involved in the state-of-the-art frame interpolation methods, this paper proposes an approach based on an end-to-end generative adversarial network. A combined loss function is employed, which jointly considers the adversarial loss (difference between data models), reconstruction loss, and motion blur degradation. The objective image quality metric values reach a PSNR of 29.22 dB and SSIM of 0.835 on the UCF101 dataset, similar to those of the state-of-the-art approach. The good visual quality is notably achieved by approximately one-fifth computational time, which entails possible real-time frame rate up-conversion. The interpolated output can be further improved by a GAN based refinement network that better maintains motion and color by image-to-image translation.

scholarly journals Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yixin Yang ◽  
Zhiqang Xiang ◽  
Jianbo Li
Keyword(s):  
New Media ◽  
Linear Function ◽  
Video Compression ◽  
Compression Algorithm ◽  
Frame Rate ◽  
Linear Estimation ◽  
Image Block ◽  
Function Model ◽  
Compression Efficiency ◽  
Low Frame Rate

When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.

Efficient Video Compression Algorithm in Secure Image Communication

2019 ◽  
Author(s):  
Bernatin T ◽  
Godwin premi M.S. ◽  
Narmadha R ◽  
Sahaya Anselin Nisha A
Keyword(s):  
Video Compression ◽  
Compression Algorithm ◽  
Image Communication ◽  
Efficient Video

scholarly journals High Resolution, High Contrast Beamformer Using Minimum Variance and Plane Wave Nonlinear Compounding with Low Complexity

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 394
Author(s):  
Xin Yan ◽  
Yanxing Qi ◽  
Yinmeng Wang ◽  
Yuanyuan Wang
Keyword(s):  
Plane Wave ◽  
Dimensional Space ◽  
Contrast Ratio ◽  
Low Complexity ◽  
Minimum Variance ◽  
Frame Rate ◽  
Channel Noise ◽  
Length Estimation ◽  
Lower Dimensional Space

The plane wave compounding (PWC) is a promising modality to improve the imaging quality and maintain the high frame rate for ultrafast ultrasound imaging. In this paper, a novel beamforming method is proposed to achieve higher resolution and contrast with low complexity. A minimum variance (MV) weight calculated by the partial generalized sidelobe canceler is adopted to beamform the receiving array signals. The dimension reduction technique is introduced to project the data into lower dimensional space, which also contributes to a large subarray length. Estimation of multi-wave receiving covariance matrix is performed and then utilized to determine only one weight. Afterwards, a fast second-order reformulation of the delay multiply and sum (DMAS) is developed as nonlinear compounding to composite the beamforming output of multiple transmissions. Simulations, phantom, in vivo, and robustness experiments were carried out to evaluate the performance of the proposed method. Compared with the delay and sum (DAS) beamformer, the proposed method achieved 86.3% narrower main lobe width and 112% higher contrast ratio in simulations. The robustness to the channel noise of the proposed method is effectively enhanced at the same time. Furthermore, it maintains a linear computational complexity, which means that it has the potential to be implemented for real-time response.

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