Flexible Variable-Rate Image Feature Compression for Edge-Cloud Systems
Feature compression is a promising direction for coding for machines. Existing methods have made substantial progress, but they require designing and training separate neural network models to meet different specifications of compression rate, performance accuracy and computational complexity. In this paper, a flexible variable-rate feature compression method is presented that can operate on a range of rates by introducing a rate control parameter as an input to the neural network model. By compressing different intermediate features of a pre-trained vision task model, the proposed method can scale the encoding complexity without changing the overall size of the model. The proposed method is more flexible than existing baselines, at the same time outperforming them in terms of the three-way trade-off between feature compression rate, vision task accuracy, and encoding complexity. We have made the source code available at https://github.com/adnan-hossain/var_feat_comp.git.
Code (2)
Tasks
Feature CompressionSimilar Papers 제목 키워드 기반
Flexible Neural Image Compression via Code Editing
Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance. However, it usually requires a dedicated encoder-decoder pair for each point on R-D curve, which greatly hind…
DecoderImage CompressionQuantizationVariable Rate Deep Image Compression with Modulated Autoencoder
Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods are optimized for a single fixed rate-distortion tradeoff. While this can be addressed by tra…
Image CompressionNavigateVideo CompressionOnce-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption
Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaption to dive…
AllImage CompressionJoint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression
Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusion models for each target bitrate, result…
Image CompressionSigVIC: Spatial Importance Guided Variable-Rate Image Compression
Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-…
DiversityImage Compression