Frequency-aware Learned Image Compression for Quality Scalability

Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal b...

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Bibliographic Details
Published in2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) pp. 1 - 5
Main Authors Choi, Hyomin, Racape, Fabien, Hamidi-Rad, Shahab, Ulhaq, Mateen, Feltman, Simon
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.12.2022
Subjects
Codecs
End-to-end compression
Image coding
learned image compression
quality scalability
Rate-distortion
Scalability
Training
Visual communication
wavelet decomposition
Wavelet transforms
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Summary:Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.
ISSN:2642-9357
DOI:10.1109/VCIP56404.2022.10008818