Confidence-based adaptive frame rate up-conversion

Kyung-Yeon Min,Dong-Gyu Sim

doi:10.1186/1687-6180-2013-13

Kyung-Yeon Min, Dong-Gyu Sim

Open Access

https://doi.org/10.1186/1687-6180-2013-13

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Abstract

In this article, we propose a new frame rate up-conversion (FRU) method for temporal quality enhancement. The proposed FRU algorithm employs gradual and adaptive motion estimation based on confidence priority for selecting more accurate motion vectors (MVs). In order to estimate accurate MVs, we adaptively alternate a search range and the order of blocks to be searched depending on the confidence level in hierarchical motion estimation. The precedence of the proposed algorithm is conducted based on the confidence level that is decided by the complexity of pixel values in a block. In addition, we perform bi-directional motion compensation and spatial linear interpolation to fill occlusion regions. In our experiments, we found that the proposed algorithm is about 2 dB better than several conventional methods. Furthermore, block artifacts and blur artifacts are significantly diminished by the proposed algorithm.

Highlights

As the technology of display devices is developed, various high-performance display devices have become available to users in the market
Bi-directional motion compensation (MC) and spatial linear interpolation are adopted in order to fill occlusion regions without blocking artifacts, because motion vectors (MVs) of some objects cannot be estimated with linear motion estimations and/or some objects exist in only one-side frame
Two layers are used in the proposed hierarchical motion estimation based on the trade-off between complexity and motion estimation accuracy

Summary

Introduction

As the technology of display devices is developed, various high-performance display devices have become available to users in the market. Bi-directional MC and spatial linear interpolation are adopted in order to fill occlusion regions without blocking artifacts, because MVs of some objects cannot be estimated with linear motion estimations and/or some objects exist in only one-side frame. The proposed algorithm performs a hierarchical OBME according to a confidence evaluation with adaptive search ranges and adaptive ordering in motion estimation.

Results

Conclusion