OSVOS is one of the best algorithms in video semantic segmentation of single-target. CBAM module is proposed in object detection and classification. This module can improve the performance of the network without adding extra computation. In view of this, this paper proposes an end-to-end network AttnVSS based on attention and VGG-16. CBAM is embedded in the shallow layer of our network. The network is first pre-trained on ImageNet, and then full connection layer is removed and fine-tuned to meet the segmentation requirements. Through the ablation experiment on DAVIS dataset, it is proved that CBAM can be embedded into semantic segmentation networks. At the same time, it can help the network to allocate "attention", focus on the most meaningful part of the input, to achieve faster and more accurate segmentation.
This paper proposes a deinterlacing algorithm based on scene change and content characteristics detection. Firstly, scene changes and video content characteristics are detected. Secondly, optimized motion estimation is performed based on scene change detection results. Thirdly, the image blocks are locally partitioned and different interpolation methods are applied. Experimental results show that the algorithm can not only improve the vertical image resolution with lower algorithm complexity, but also obtain high-quality progressive sequences for interlaced video sequences of different video content.
In order to eliminate the effect of edge aliasing and blurring caused by deinterlacing algorithm based on traditional edge detection, this paper proposes a novel edge direction determination method. On the basic of the preliminary direction judgment obtained from the absolute difference, the reliability judgment of the direction is added. The correlation between the vertical direction of the current pixel and the pixel mean of the remaining four directions is used to obtain a direction determination criterion, and then interpolation is performed by means of median filtering. The experimental results indicate that the PSNR and visual quality of the proposed algorithm outperforms other existing methods, whether its objective evaluation or subjective evaluation.
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