Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

• URL: http://arxiv.org/abs/2308.07733v1
• Date: Tue, 15 Aug 2023 12:17:46 GMT
• Title: Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
• Authors: Yue Lv, Jinxi Xiang, Jun Zhang, Wenming Yang, Xiao Han, Wei Yang
• Abstract summary: We propose a low-rank adaptation approach to address the rate-distortion drop observed in out-of-domain datasets. Our proposed method exhibits universality across diverse image datasets.
• Score: 33.92792778925365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The latest advancements in neural image compression show great potential in surpassing the rate-distortion performance of conventional standard codecs. Nevertheless, there exists an indelible domain gap between the datasets utilized for training (i.e., natural images) and those utilized for inference (e.g., artistic images). Our proposal involves a low-rank adaptation approach aimed at addressing the rate-distortion drop observed in out-of-domain datasets. Specifically, we perform low-rank matrix decomposition to update certain adaptation parameters of the client's decoder. These updated parameters, along with image latents, are encoded into a bitstream and transmitted to the decoder in practical scenarios. Due to the low-rank constraint imposed on the adaptation parameters, the resulting bit rate overhead is small. Furthermore, the bit rate allocation of low-rank adaptation is \emph{non-trivial}, considering the diverse inputs require varying adaptation bitstreams. We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation. The dynamic adaptation network is optimized end-to-end using rate-distortion loss. Our proposed method exhibits universality across diverse image datasets. Extensive results demonstrate that this paradigm significantly mitigates the domain gap, surpassing non-adaptive methods with an average BD-rate improvement of approximately $19\%$ across out-of-domain images. Furthermore, it outperforms the most advanced instance adaptive methods by roughly $5\%$ BD-rate. Ablation studies confirm our method's ability to universally enhance various image compression architectures.

Related papers

• A Rate-Distortion-Classification Approach for Lossy Image Compression [0.0]
  In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression.
  arXiv  Detail & Related papers  (2024-05-06T14:11:36Z)
• Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis [10.428185253933004]
  We replace Gaussian decoders with a non-isotropic diffusion model at the decoder side. Our framework is equipped with a novel entropy model that accurately models probability distribution latent representation. Our experiments demonstrate that our framework yields better perceptual quality compared to cutting-edge generative entropy-based codecs.
  arXiv  Detail & Related papers  (2024-03-24T18:33:16Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression [60.689646881479064]
  We propose a progressive learning paradigm for transformer-based variable-rate image compression. Inspired by visual prompt tuning, we use LPM to extract prompts for input images and hidden features at the encoder side and decoder side, respectively. Our model outperforms all current variable image methods in terms of rate-distortion performance and approaches the state-of-the-art fixed image compression methods trained from scratch.
  arXiv  Detail & Related papers  (2023-11-23T08:29:32Z)
• High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation [24.379052026260034]
  We propose the Invertible Activation Transformation (IAT) module to tackle the issue of high-fidelity fine variable-rate image compression. IAT and QLevel together give the image compression model the ability of fine variable-rate control while better maintaining the image fidelity. Our method outperforms the state-of-the-art variable-rate image compression method by a large margin, especially after multiple re-encodings.
  arXiv  Detail & Related papers  (2022-09-12T07:14:07Z)
• Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression [2.1485350418225244]
  End-to-end deep trainable models are about to exceed the performance of the traditional handcrafted compression techniques on videos and images. We propose a simple yet efficient instance-based parameterization method to reduce this amortization gap at a minor cost.
  arXiv  Detail & Related papers  (2022-09-02T11:43:45Z)
• Estimating the Resize Parameter in End-to-end Learned Image Compression [50.20567320015102]
  We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our results show that our new resizing parameter estimation framework can provide Bjontegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
  arXiv  Detail & Related papers  (2022-04-26T01:35:02Z)
• 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)
• Substitutional Neural Image Compression [48.20906717052056]
  Substitutional Neural Image Compression (SNIC) is a general approach for enhancing any neural image compression model. It boosts compression performance toward a flexible distortion metric and enables bit-rate control using a single model instance.
  arXiv  Detail & Related papers  (2021-05-16T20:53:31Z)

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.