Lossy Image Compression with Normalizing Flows

• URL: http://arxiv.org/abs/2008.10486v1
• Date: Mon, 24 Aug 2020 14:46:23 GMT
• Title: Lossy Image Compression with Normalizing Flows
• Authors: Leonhard Helminger, Abdelaziz Djelouah, Markus Gross, Christopher Schroers
• Abstract summary: State-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space. In contrast, traditional approaches in image compression allow for a larger range of quality levels.
• Score: 19.817005399746467
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been established and refined over many decades. However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization. Due to that, they inherently limit the range of quality levels that can be covered. In contrast, traditional approaches in image compression allow for a larger range of quality levels. Interestingly, they employ an invertible transformation before performing the quantization step which explicitly discards information. Inspired by this, we propose a deep image compression method that is able to go from low bit-rates to near lossless quality by leveraging normalizing flows to learn a bijective mapping from the image space to a latent representation. In addition to this, we demonstrate further advantages unique to our solution, such as the ability to maintain constant quality results through re-encoding, even when performed multiple times. To the best of our knowledge, this is the first work to explore the opportunities for leveraging normalizing flows for lossy image compression.

Related papers

• DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding [27.875207681547074]
  progressive image coding (PIC) aims to compress various qualities of images into a single bitstream. Research on neural network (NN)-based PIC is in its early stages. We propose an NN-based progressive coding method that firstly utilizes learned quantization step sizes via learning for each quantization layer.
  arXiv  Detail & Related papers  (2024-08-22T06:32:53Z)
• HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression [51.04820313355164]
  HyrbidFlow combines the continuous-feature-based and codebook-based streams to achieve both high perceptual quality and high fidelity under extreme lows. Experimental results demonstrate superior performance across several datasets under extremely lows.
  arXiv  Detail & Related papers  (2024-04-20T13:19:08Z)
• 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)
• Multiscale Augmented Normalizing Flows for Image Compression [17.441496966834933]
  We present a novel concept, which adapts the hierarchical latent space for augmented normalizing flows, an invertible latent variable model. Our best performing model achieved average rate savings of more than 7% over comparable single-scale models.
  arXiv  Detail & Related papers  (2023-05-09T13:42:43Z)
• Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training [90.76576712433595]
  Applying lossy compression on images processed by deep neural networks can lead to significant accuracy degradation. Inspired by the curriculum learning paradigm, we present a novel training approach called curriculum pre-training (CPT) for crowd counting on compressed images.
  arXiv  Detail & Related papers  (2022-08-15T08:43:21Z)
• 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)
• Enhanced Invertible Encoding for Learned Image Compression [40.21904131503064]
  In this paper, we propose an enhanced Invertible. Network with invertible neural networks (INNs) to largely mitigate the information loss problem for better compression. Experimental results on the Kodak, CLIC, and Tecnick datasets show that our method outperforms the existing learned image compression methods.
  arXiv  Detail & Related papers  (2021-08-08T17:32:10Z)
• How to Exploit the Transferability of Learned Image Compression to Conventional Codecs [25.622863999901874]
  We show how learned image coding can be used as a surrogate to optimize an image for encoding. Our approach can remodel a conventional image to adjust for the MS-SSIM distortion with over 20% rate improvement without any decoding overhead.
  arXiv  Detail & Related papers  (2020-12-03T12:34:51Z)
• Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images [54.40852143927333]
  Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. We propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images.
  arXiv  Detail & Related papers  (2020-06-30T07:38:47Z)
• 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)
• Quantization Guided JPEG Artifact Correction [69.04777875711646]
  We develop a novel architecture for artifact correction using the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.
  arXiv  Detail & Related papers  (2020-04-17T00:10:08Z)

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.