Adaptive Temporal Frame Interpolation Algorithm for Frame Rate Up-Conversion

2020 ◽  
Vol 9 (3) ◽  
pp. 17-21
Author(s):  
Denis Vranjes ◽  
Snjezana Rimac-Drlje ◽  
Mario Vranjes
Keyword(s):  
Frame Rate ◽  
Interpolation Algorithm ◽  
Frame Interpolation ◽  
Temporal Frame ◽  
Frame Rate Up Conversion

NEW MULTIPLE FRAME MOTION COMPENSATED INTERPOLATION ALGORITHM FOR FRAME RATE UP-CONVERSION

2008 ◽  
Vol 17 (05) ◽  
pp. 835-844
Author(s):  
ZHUO XUE ◽  
KOK-KEONG LOO ◽  
JOHN COSMAS ◽  
PIK-YEE YIP
Keyword(s):  
Hardware Implementation ◽  
Frame Rate ◽  
Perceptual Quality ◽  
Interpolation Algorithm ◽  
Frame Interpolation ◽  
Multiple Frames ◽  
Multiple Frame ◽  
Frame Rate Up Conversion ◽  
Frame Motion ◽  
Multiple Reference

In this paper, a new frame rate up-conversion (FRC) using Backward Multiple Reference Frame Motion (BMRF) compensated interpolation (MCI) algorithm is proposed. The BMRF algorithm has been compared with two most common MCI methods, Temporal Linear (TLIN) and Bidirectional MCI (BID) used in FRC, in the aspect of PSNR, perceptual quality, and computational complexity. The simulation results clearly showed that BMRF presented a trade-off solution between computation and interpolation quality. By using simple weighted frame interpolation between multiple frames, it reduced computation rapidly while keeping similar interpolation quality when compared to the BID method. The simplicity of the BMRF algorithm made it suitable for either software or hardware implementation essential for real-time FRC applications.

Motion-Compensated Frame Rate Up-Conversion—Part II: New Algorithms for Frame Interpolation

2010 ◽  
Vol 56 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Demin Wang ◽  
André Vincent ◽  
Philip Blanchfield ◽  
Robert Klepko
Keyword(s):  
Frame Rate ◽  
Frame Interpolation ◽  
Frame Rate Up Conversion ◽  
New Algorithms

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.

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