In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is...
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In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texturecomplexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texturecomplexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texturecomplexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.
The third generation of Audio Video coding Standard (AVS3) is an emerging video coding standard that surpasses High Efficiency Video Coding (HEVC). AVS3 allows flexible block subdivision by applying Quad-Tree (QT), Bi...
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The third generation of Audio Video coding Standard (AVS3) is an emerging video coding standard that surpasses High Efficiency Video Coding (HEVC). AVS3 allows flexible block subdivision by applying Quad-Tree (QT), Binary-Tree (BT) plus Extend Quad-Tree (EQT) partition structure, while the increased flexibility comes at the cost of enormous coding complexity. In this paper, an ingenious early termination mechanism is proposed to skip unnecessary exhaustive searches of the whole tree branches. We carry out a series of restrictive measures based on Coding Unit (CU) size and iteration status to make the sophisticated EQT split mode concentrate more on small CUs with complex texture structure. Historical QTBT partition information is also adopted as an important factor to skip EQT split mode in advance. Meanwhile, a fast CU partition algorithm based on the gradient is proposed to skip horizontal or vertical BT/EQT partition of CUs with prominent texture structure in another direction, in which early termination can be directly conducted in homogenous areas at the same time. Extensive experiments demonstrate that the proposed method can save 43% encoding time with only 0.53% BDBR increase on average under All Intra(AI) configuration, which outperforms the preexisting fast algorithms.
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