| ZHANG Yangmei(张扬眉),ZHANG Xishan,ZHANG Shuo,LI Jintao.[J].高技术通讯(英文),2025,31(2):194~203 |
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| Optimized algorithm for image semantic segmentation compression algorithm in video surveillance scenarios |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 02. 009 |
| 中文关键词: |
| 英文关键词: macroblock encoding, semantic segmentation, segmentation compression |
| 基金项目: |
| Author Name | Affiliation | | ZHANG Yangmei(张扬眉) | (State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China) | | ZHANG Xishan | | | ZHANG Shuo | | | LI Jintao | |
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| 中文摘要: |
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| 英文摘要: |
| In recent years, video coding has been widely applied in the field of video image processing to remove redundant information and improve data transmission efficiency. However, during the video coding process,irrelevant objects such as background elements are often encoded due to environmental disturbances, resulting in the wastage of computational resources. Existing research on video coding efficiency optimization primarily focuses on optimizing encoding units during intra-frame or inter frame prediction after the generation of coding units, neglecting the optimization of video images before coding unit generation. To address this challenge, This work proposes an image semantic segmentation compression algorithm based on macroblock encoding, called image semantic segmentation compression algorithm based on macroblock encoding (ISSC-ME), which consists of three modules.(1)The semantic label generation module generates interesting object labels using a grid-based approach to reduce redundant coding of consecutive frames. (2)The image segmentation network module generates a semantic segmentation image using U-Net. (3)The macroblock coding module, is a block segmentation-based video encoding and decoding algorithm used to compress images and improve video transmission efficiency. Experimental results show that the proposed image semantic segmentation optimization algorithm can reduce the computational costs, and improve the overall accuracy by 1. 00% and the mean intersection over union (IoU) by 1. 20% . In addition, the proposed compression algorithm utilizes macroblock fusion, resulting in the image compression rate achieving 80. 64% . It has been proven that the proposed algorithm greatly reduces data storage and transmission, and enables fast image compression processing at the millisecond level. |
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