Lightweight Width-Depth Scalable Implicit Neural Representation for Progressive Image Compression

Abstract

Image compression approaches using implicit neural representation (INR) have recently gained attention for their lightweight nature, compactness, and fast decoding, showing promise for edge computing in consumer devices. Specifically, INR-based image compression methods implicitly store each image within a lightweight neural network, which serves as a compact representation of the image. However, most existing methods are limited to representing single-quality images with fixed-size models, which necessitates training separate models independently for images at varying quality levels, leading to additional training and storage costs. To tackle this problem, we propose a progressive image compression method based on Width-Depth Scalable Implicit Neural Representation (WDS-INR), which are composed of executable sub-networks of varying scales. By adjusting the scale of the sub-networks, WDS-INR can represent images at different quality levels while supporting progressive transmission. The scalable architecture of WDS-INR makes it well-suited for deployment on mobile and IoTs devices. Furthermore, we propose a band-limited initialization scheme that enhances both the representation capabilities and training stability of the WDS-INR. Finally, we introduce a meta-learning approach to the base sub-network to accelerate encoding $(4 \times \text { faster})$ . Experimental results demonstrate that the proposed method outperforms the baseline in rate-distortion performance $(+ 0.28~dB {~\text {PSNR}})$ , while enabling scalable bit-rates with progressive decoding.