Frame Rate Up-Conversion Based Motion Vector Derivation for Hybrid Video Coding

2017 ◽  
Author(s):  
Xiang Li ◽  
Jianle Chen ◽  
Marta Karczewicz
Keyword(s):  
Video Coding ◽  
Motion Vector ◽  
Frame Rate ◽  
Frame Rate Up Conversion

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.

A Novel Frame Rate Up-Conversion Algorithm Based on Soft Threshold Bandelet Transform

2016 ◽  
Vol 30 (07) ◽  
pp. 1650017 ◽  
Author(s):  
Mingjun Deng ◽  
Fengchun Tian ◽  
Jian Ran ◽  
Zhiyong Shi ◽  
Li Zhang
Keyword(s):  
Motion Vector ◽  
Block Matching ◽  
Frame Rate ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Block Motion ◽  
Soft Threshold ◽  
Frame Rate Up Conversion ◽  
Vector Median

The most important part of frame rate up-conversion (FRUC) is block matching. The geometric properties of the image were not taken into consideration in traditional block matching algorithm, so the matching result of motion estimation cannot reach the optimal. A novel FRUC algorithm based on Bandelet was proposed in this paper. The algorithm includes: Firstly, a soft threshold Bandelet transform of matching block was performed. The optimal matching block was determined through detection of direction similarity and Bandelet coefficient similarity; secondly, vector median filtering (VMF) and overlapped block motion compensation (OBMC) were carried out by adopting motion vector to realize interpolated frame algorithm. Experimental results show that the FRUC algorithm based on Bandelet can further promote the quality of FRUC.

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 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.

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