scholarly journals A Low-Complex Frame Rate Up-Conversion with Edge-Preserved Filtering

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 156 ◽  
Author(s):  
Ran Li ◽  
Wendan Ma ◽  
Yanling Li ◽  
Lei You
Keyword(s):  
Computational Complexity ◽  
Motion Vector ◽  
Frame Rate ◽  
Video Frame ◽  
Continuous Increase ◽  
Video Frames ◽  
Motion Vector Field ◽  
Frequency Components ◽  
Speed Up ◽  
Frame Rate Up Conversion

The improvement of resolution of digital video requires a continuous increase of computation invested into Frame Rate Up-Conversion (FRUC). In this paper, we combine the advantages of Edge-Preserved Filtering (EPF) and Bidirectional Motion Estimation (BME) in an attempt to reduce the computational complexity. The inaccuracy of BME results from the existing similar structures in the texture regions, which can be avoided by using EPF to remove the texture details of video frames. EPF filters out by the high-frequency components, so each video frame can be subsampled before BME, at the same time, with the least accuracy degradation. EPF also preserves the edges, which prevents the deformation of object in the process of subsampling. Besides, we use predictive search to reduce the redundant search points according to the local smoothness of Motion Vector Field (MVF) to speed up BME. The experimental results show that the proposed FRUC algorithm brings good objective and subjective qualities of the interpolated frames with a low computational complexity.

scholarly journals A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion

2019 ◽  
Vol 11 (2) ◽  
pp. 26 ◽  
Author(s):  
Yanli Li ◽  
Wendan Ma ◽  
Yue Han
Keyword(s):  
Motion Vector ◽  
Spatial Prediction ◽  
Frame Rate ◽  
Prediction Algorithm ◽  
Predictive Algorithm ◽  
Motion Vector Field ◽  
Frame Rate Up Conversion ◽  
Multimedia Internet Of Things ◽  
Basic Blocks

In Multimedia Internet of Things (IoT), in order to reduce the bandwidth consumption of wireless channels, Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is often used to support the low-bitrate video communication. In this paper, we propose a spatial predictive algorithm which is used to improve the performance of MC-FRUC. The core of the proposed algorithm is a predictive model to split a frame into two kinds of blocks: basic blocks and absent blocks. Then an improved bilateral motion estimation is proposed to compute the Motion Vectors (MVs) of basic blocks. Finally, with the spatial correlation of Motion Vector Field (MVF), the MV of an absent block is predicted based on the MVs of its neighboring basic blocks. Experimental results show that the proposed spatial prediction algorithm can improve both the objective and the subjective quality of the interpolated frame, with a low computational complexity.

Hierarchical prediction-based motion vector refinement for video frame-rate up-conversion

2018 ◽  
Vol 17 (2) ◽  
pp. 259-273 ◽  
Author(s):  
Jiale He ◽  
Gaobo Yang ◽  
Jingyu Song ◽  
Xiangling Ding ◽  
Ran Li
Keyword(s):  
Motion Vector ◽  
Frame Rate ◽  
Video Frame ◽  
Frame Rate Up Conversion ◽  
Video Frame Rate

scholarly journals Motion-Compensated Frame Interpolation Using Cellular Automata-Based Motion Vector Smoothing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Ran Li ◽  
Ying Yin ◽  
Fengyuan Sun ◽  
Yanling Li ◽  
Lei You
Keyword(s):  
Cellular Automata ◽  
Motion Vector ◽  
Video Sequences ◽  
Frame Interpolation ◽  
Temporal Domain ◽  
Video Frames ◽  
Motion Compensated Frame Interpolation ◽  
Motion Vector Field ◽  
The Common ◽  
Temporal Symmetry

Motion-Compensated Frame Interpolation (MCFI) is one of the common temporal-domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. To overcome this problem, we propose a Cellular Automata-based MVS (CA-MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. By performing CA-based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms.

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