Abstract
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.
Highlights
Films are played with 24 frames per second and TV programs are broadcasted with a standard frame rate of 30 Hz
Experimental results demonstrate that the proposed Spatial Prediction-based Motion-Compensated Frame Interpolation (SP-MCFI) algorithm generates a pleasant up-converted video, and it has a low computational complexity
The performance of the proposed SP-MCFI algorithm is evaluated by testing on different video sequences, and the results are compared with recent state-of-the-arts Motion Estimation (ME) algorithms Dual-ME from [24] and DS-ME from [25]
Summary
Films are played with 24 frames per second and TV programs are broadcasted with a standard frame rate of 30 Hz. MC-FRUC, which is gaining extensive attention from scholars in recent years [1,2,3,4,5], is a video processing technique interpolating several new frames between two adjacent original frames It has a standard flow including Motion Estimation (ME), Motion Vector Smoothing (MVS), Motion Vector Mapping (MVM) and Motion-Compensated Interpolation (MCI), among which the former three are combined to provide the Motion Vector Field (MVF) of the middle frame, and MCI is used to interpolate the new frame according to the above MVF [6]. Fractal interpolation can be performed to predict the pixels at fractional coordinates and effectively reduces blurring and block artifacts by providing a pleasant zoom and slow motion [18]. Experimental results demonstrate that the proposed SP-MCFI algorithm generates a pleasant up-converted video, and it has a low computational complexity
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