Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing

Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However,...

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Veröffentlicht in:IEEE Global Conference on Consumer Electronics S. 166 - 167
Hauptverfasser: Shindo, Takahiro, Tatsumi, Yui, Watanabe, Taiju, Watanabe, Hiroshi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 29.10.2024
Schlagworte:
Consumer electronics
Decoding
Image coding
image coding for machines
image compression
Image quality
Image recognition
Measurement
Noise
post-processing
Refining
scalable image coding
ISSN:2693-0854
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Zusammenfassung:Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However, no research has yet incorporated the post-processing commonly used in popular image compression schemes into scalable image coding method for humans and machines. In this paper, we propose a method to enhance the quality of decoded images for humans by integrating post-processing into scalable coding scheme. Experimental results show that the post-processing improves compression performance. Furthermore, the effectiveness of the proposed method is validated through comparisons with traditional methods.
ISSN:2693-0854
DOI:10.1109/GCCE62371.2024.10760327