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Neural Image Compression with Regional Decoding
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DOI：10.1145/3708347.png)
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Abstract
En 中文
As advancements are made in technology such as AR/VR and high-resolution photography, there is a growing need for a function in image compression named regional decoding. This function lets an image be encoded as a whole, but allows for an arbitrary region to be decoded using only a small part of the bitstream. However, existing neural image compression methods lack support for this crucial functionality. In this article, we propose a novel approach called the slicing en/decoder, which addresses the need for regional decoding while maintaining performance on par with state-of-the-art methods. Our approach is based on the insight that, during the compression process, local information within pixels holds greater importance than global information. By leveraging this understanding, we divide the image into different bitstreams according to cross-boundary patterns. Consequently, for a selected region, our method can intelligently choose specific portions of the bitstreams to decode only that particular region of interest. Furthermore, we extend the application of our method to 360 degrees image compression, allowing for efficient encoding and decoding of immersive visual content. Moreover, our proposed technique offers the capability to decode regions identically, which paves the way for future advancements in regional video decoding. Our experimental results demonstrate that our method maintains performance on par with state-of-the-art methods while providing the functionality of regional decoding. In conclusion, this article presents a significant step forward in image compression technology, offering enhanced flexibility and efficiency for emerging applications in digital media.
Keywords:
Image compression
Neural networks
Regional decoding
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