Rate-Distortion-Cognition Controllable Versatile Neural Image Compression

• URL: http://arxiv.org/abs/2407.11700v2
• Date: Wed, 17 Jul 2024 06:26:20 GMT
• Title: Rate-Distortion-Cognition Controllable Versatile Neural Image Compression
• Authors: Jinming Liu, Ruoyu Feng, Yunpeng Qi, Qiuyu Chen, Zhibo Chen, Wenjun Zeng, Xin Jin,
• Abstract summary: We propose a rate-distortion-cognition controllable versatile image compression method. Our method yields satisfactory ICM performance and flexible Rate-DistortionCognition controlling.
• Score: 47.72668401825835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the field of Image Coding for Machines (ICM) has garnered heightened interest and significant advances thanks to the rapid progress of learning-based techniques for image compression and analysis. Previous studies often require training separate codecs to support various bitrate levels, machine tasks, and networks, thus lacking both flexibility and practicality. To address these challenges, we propose a rate-distortion-cognition controllable versatile image compression, which method allows the users to adjust the bitrate (i.e., Rate), image reconstruction quality (i.e., Distortion), and machine task accuracy (i.e., Cognition) with a single neural model, achieving ultra-controllability. Specifically, we first introduce a cognition-oriented loss in the primary compression branch to train a codec for diverse machine tasks. This branch attains variable bitrate by regulating quantization degree through the latent code channels. To further enhance the quality of the reconstructed images, we employ an auxiliary branch to supplement residual information with a scalable bitstream. Ultimately, two branches use a `$\beta x + (1 - \beta) y$' interpolation strategy to achieve a balanced cognition-distortion trade-off. Extensive experiments demonstrate that our method yields satisfactory ICM performance and flexible Rate-Distortion-Cognition controlling.

Related papers

• Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption [57.056311855630916]
  We propose a Controllable Generative Image Compression framework, Control-GIC. It is capable of fine-grained adaption across a broad spectrum while ensuring high-fidelity and generality compression. We develop a conditional conditionalization that can trace back to historic encoded multi-granularity representations.
  arXiv  Detail & Related papers  (2024-06-02T14:22:09Z)
• Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression [58.618625678054826]
  This study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.
  arXiv  Detail & Related papers  (2024-01-25T08:11:27Z)
• Extreme Image Compression using Fine-tuned VQGANs [43.43014096929809]
  We introduce vector quantization (VQ)-based generative models into the image compression domain. The codebook learned by the VQGAN model yields a strong expressive capacity. The proposed framework outperforms state-of-the-art codecs in terms of perceptual quality-oriented metrics.
  arXiv  Detail & Related papers  (2023-07-17T06:14:19Z)
• Improving Multi-generation Robustness of Learned Image Compression [16.86614420872084]
  We show that LIC can achieve comparable performance to the first compression of BPG even after 50 times reencoding without any change of the network structure.
  arXiv  Detail & Related papers  (2022-10-31T03:26:11Z)
• Preprocessing Enhanced Image Compression for Machine Vision [14.895698385236937]
  We propose a preprocessing enhanced image compression method for machine vision tasks. Our framework is built upon the traditional non-differential codecs. Experimental results show our method achieves a better tradeoff between the coding and the performance of the downstream machine vision tasks by saving about 20%.
  arXiv  Detail & Related papers  (2022-06-12T03:36:38Z)
• Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder [73.48927855855219]
  We propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends. Experiments demonstrate that our approach successfully improves the rate-distortion performance over JPEG across various quality metrics.
  arXiv  Detail & Related papers  (2022-01-27T20:20:03Z)
• Implicit Neural Representations for Image Compression [103.78615661013623]
  Implicit Neural Representations (INRs) have gained attention as a novel and effective representation for various data types. We propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding. We find that our approach to source compression with INRs vastly outperforms similar prior work.
  arXiv  Detail & Related papers  (2021-12-08T13:02:53Z)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.