SIMAGE‐Dedup: Leveraging Semantic Segmentation for Efficient Similar Image Deduplication

ABSTRACT With the rapid growth of image storage requirements, image deduplication has become a necessary technology to reduce storage costs by removing redundant images. Here, we propose Simage‐Dedup, a similar image deduplication scheme based on semantic segmentation. The scheme employs scalable semantic segmentation for fine‐grained deduplication and uses a semantic‐aware similarity detection method to analyze local semantic block features, enabling quick identification and alignment of similar images. Lossless compression strategies are applied to the aligned image blocks to mitigate the impact of local misalignment on compression efficiency. Finally, the images and their difference information are stored in the cloud to minimize overall storage requirements. To evaluate the performance of Simage‐Dedup, we developed a prototype system. It improves throughput by 1.8 to 3.4 times and compression ratio by 1.3 to 1.6 times, demonstrating its excellent performance.