True motion compensation with feature detection for frame rate up-conversion

Kim, Kyungah; Kim, Minwoo; Kim, Deokho; Ro, Won Woo

doi:10.1109/icip.2015.7351204

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…The main issue of these pixel-based ME is the high computation complexity, and it is not suitable for real-time applications. Except for block-based and pixel-based ME, some traditional VFI methods also employed region-based [25], feature-based [26] or mesh-based ME [27], and the main idea was to classify pixels by considering the content in the video scene.…”

Section: Traditional Video Frame Interpolation Methodsmentioning

confidence: 99%

Video frame interpolation via spatial multi‐scale modelling

Qu,

Liu,

Cui

et al. 2024

IET Computer Vision

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Video frame interpolation (VFI) is a technique that synthesises intermediate frames between adjacent original video frames to enhance the temporal super‐resolution of the video. However, existing methods usually rely on heavy model architectures with a large number of parameters. The authors introduce an efficient VFI network based on multiple lightweight convolutional units and a Local three‐scale encoding (LTSE) structure. In particular, the authors introduce a LTSE structure with two‐level attention cascades. This design is tailored to enhance the efficient capture of details and contextual information across diverse scales in images. Secondly, the authors introduce recurrent convolutional layers (RCL) and residual operations, designing the recurrent residual convolutional unit to optimise the LTSE structure. Additionally, a lightweight convolutional unit named separable recurrent residual convolutional unit is introduced to reduce the model parameters. Finally, the authors obtain the three‐scale decoding features from the decoder and warp them for a set of three‐scale pre‐warped maps. The authors fuse them into the synthesis network to generate high‐quality interpolated frames. The experimental results indicate that the proposed approach achieves superior performance with fewer model parameters.

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Section: Traditional Video Frame Interpolation Methodsmentioning

confidence: 99%