Multi-frame interpolation of Bayer images using optical flow

2020 ◽  
Author(s):  
Toshiki Sato ◽  
Norishige Fukushima
Keyword(s):  
Optical Flow ◽  
Frame Interpolation

scholarly journals A Fast 4K Video Frame Interpolation Using a Multi-Scale Optical Flow Reconstruction Network

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1251 ◽  
Author(s):  
Ahn ◽  
Jeong ◽  
Kim ◽  
Kwon ◽  
Yoo
Keyword(s):  
High Resolution ◽  
Optical Flow ◽  
State Of The Art ◽  
Interpolation Method ◽  
Video Frame ◽  
Frame Interpolation ◽  
Multi Scale ◽  
Reconstruction Scheme ◽  
Flow Reconstruction

Recently, video frame interpolation research developed with a convolutional neural network has shown remarkable results. However, these methods demand huge amounts of memory and run time for high-resolution videos, and are unable to process a 4K frame in a single pass. In this paper, we propose a fast 4K video frame interpolation method, based upon a multi-scale optical flow reconstruction scheme. The proposed method predicts low resolution bi-directional optical flow, and reconstructs it into high resolution. We also proposed consistency and multi-scale smoothness loss to enhance the quality of the predicted optical flow. Furthermore, we use adversarial loss to make the interpolated frame more seamless and natural. We demonstrated that the proposed method outperforms the existing state-of-the-art methods in quantitative evaluation, while it runs up to 4.39× faster than those methods for 4K videos.

scholarly journals Frame interpolation in video stream using optical flow methods

2020 ◽  
Vol 1488 ◽  
pp. 012024
Author(s):  
S Lyasheva ◽  
R Rakhmankulov ◽  
M Shleymovich
Keyword(s):  
Optical Flow ◽  
Video Stream ◽  
Frame Interpolation

scholarly journals Enhanced Real-Time Intermediate Flow Estimation for Video Frame Interpolation

2021 ◽  
Author(s):  
Minseop Kim ◽  
Haechul Choi
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Estimation Method ◽  
Frame Rate ◽  
Video Frame ◽  
High Quality ◽  
Frame Interpolation ◽  
Real Time Processing ◽  
High Frame Rate ◽  
Flow Estimation

Recently, the demand for high-quality video content has rapidly been increasing, led by the development of network technology and the growth in video streaming platforms. In particular, displays with a high refresh rate, such as 120 Hz, have become popular. However, the visual quality is only enhanced if the video stream is produced at the same high frame rate. For the high quality, conventional videos with a low frame rate should be converted into a high frame rate in real time. This paper introduces a bidirectional intermediate flow estimation method for real-time video frame interpolation. A bidirectional intermediate optical flow is directly estimated to predict an accurate intermediate frame. For real-time processing, multiple frames are interpolated with a single intermediate optical flow and parts of the network are implemented in 16-bit floating-point precision. Perceptual loss is also applied to improve the cognitive performance of the interpolated frames. The experimental results showed a high prediction accuracy of 35.54 dB on the Vimeo90K triplet benchmark dataset. The interpolation speed of 84 fps was achieved for 480p resolution.

scholarly journals Subjective annotation for a frame interpolation benchmark using artefact amplification

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Hui Men ◽  
Vlad Hosu ◽  
Hanhe Lin ◽  
Andrés Bruhn ◽  
Dietmar Saupe
Keyword(s):  
Image Quality ◽  
Optical Flow ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Visual Quality ◽  
Ground Truth ◽  
Paired Comparisons ◽  
Frame Interpolation ◽  
Full Reference

Abstract Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation and then comparing the interpolated frames with the actual frames. In the latter case, objective quality measures such as the mean squared error are typically employed. However, it is well known that for image quality assessment, the actual quality experienced by the user cannot be fully deduced from such simple measures. Hence, we conducted a subjective quality assessment crowdscouring study for the interpolated frames provided by one of the optical flow benchmarks, the Middlebury benchmark. It contains interpolated frames from 155 methods applied to each of 8 contents. For this purpose, we collected forced-choice paired comparisons between interpolated images and corresponding ground truth. To increase the sensitivity of observers when judging minute difference in paired comparisons we introduced a new method to the field of full-reference quality assessment, called artefact amplification. From the crowdsourcing data (3720 comparisons of 20 votes each) we reconstructed absolute quality scale values according to Thurstone’s model. As a result, we obtained a re-ranking of the 155 participating algorithms w.r.t. the visual quality of the interpolated frames. This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images. As a first step, we proposed such a new full-reference method, called WAE-IQA, which weights the local differences between an interpolated image and its ground truth.

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