Related papers: Efficient Masked Image Compression with Position-Indexed Self-Attention

Efficient Masked Image Compression with Position-Indexed Self-Attention

URL: http://arxiv.org/abs/2504.12923v1
Date: Thu, 17 Apr 2025 13:12:39 GMT
Title: Efficient Masked Image Compression with Position-Indexed Self-Attention
Authors: Chengjie Dai, Tiantian Song, Hui Tang, Fangdong Chen, Bowei Yang, Guanghua Song,
Abstract summary: We propose an image compression method based on a position-indexed self-attention mechanism.<n>Compared to existing semantic-structured compression methods, our approach can significantly reduce computational costs.
Score: 6.64044416324419
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In recent years, image compression for high-level vision tasks has attracted considerable attention from researchers. Given that object information in images plays a far more crucial role in downstream tasks than background information, some studies have proposed semantically structuring the bitstream to selectively transmit and reconstruct only the information required by these tasks. However, such methods structure the bitstream after encoding, meaning that the coding process still relies on the entire image, even though much of the encoded information will not be transmitted. This leads to redundant computations. Traditional image compression methods require a two-dimensional image as input, and even if the unimportant regions of the image are set to zero by applying a semantic mask, these regions still participate in subsequent computations as part of the image. To address such limitations, we propose an image compression method based on a position-indexed self-attention mechanism that encodes and decodes only the visible parts of the masked image. Compared to existing semantic-structured compression methods, our approach can significantly reduce computational costs.

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