Content-aware Masked Image Modeling Transformer for Stereo Image Compression

• URL: http://arxiv.org/abs/2403.08505v2
• Date: Wed, 20 Mar 2024 02:35:57 GMT
• Title: Content-aware Masked Image Modeling Transformer for Stereo Image Compression
• Authors: Xinjie Zhang, Shenyuan Gao, Zhening Liu, Jiawei Shao, Xingtong Ge, Dailan He, Tongda Xu, Yan Wang, Jun Zhang,
• Abstract summary: We propose a stereo image compression framework, named CAMSIC. CAMSIC transforms each image to latent representation and employs a powerful decoder-free Transformer entropy model. Experiments show that our framework achieves state-of-the-art rate-distortion performance on two stereo image datasets.
• Score: 15.819672238043786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations. However, those entropy models struggle to effectively capture the spatial-disparity characteristics inherent in stereo images, which leads to suboptimal rate-distortion results. In this paper, we propose a stereo image compression framework, named CAMSIC. CAMSIC independently transforms each image to latent representation and employs a powerful decoder-free Transformer entropy model to capture both spatial and disparity dependencies, by introducing a novel content-aware masked image modeling (MIM) technique. Our content-aware MIM facilitates efficient bidirectional interaction between prior information and estimated tokens, which naturally obviates the need for an extra Transformer decoder. Experiments show that our stereo image codec achieves state-of-the-art rate-distortion performance on two stereo image datasets Cityscapes and InStereo2K with fast encoding and decoding speed.

Related papers

• When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding [112.44822009714461]
  Cross-Modality Video Coding (CMVC) is a pioneering approach to explore multimodality representation and video generative models in video coding. During decoding, previously encoded components and video generation models are leveraged to create multiple encoding-decoding modes. Experiments indicate that TT2V achieves effective semantic reconstruction, while IT2V exhibits competitive perceptual consistency.
  arXiv  Detail & Related papers  (2024-08-15T11:36:18Z)
• Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model [11.959608742884408]
  BiSIC is a symmetric stereo image compression architecture. We propose a 3D convolution based backbone to capture local features and incorporate bidirectional attention blocks to exploit global features. Our proposed BiSIC outperforms conventional image/video compression standards.
  arXiv  Detail & Related papers  (2024-07-15T11:36:22Z)
• 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)
• ECSIC: Epipolar Cross Attention for Stereo Image Compression [5.024813922014978]
  ECSIC achieves state-of-the-art performance in stereo image compression on the two popular stereo image datasets Cityscapes and InStereo2k.
  arXiv  Detail & Related papers  (2023-07-18T11:46:31Z)
• AICT: An Adaptive Image Compression Transformer [18.05997169440533]
  We propose a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT) The proposed ICT can capture both global and local contexts from the latent representations. We leverage a learnable scaling module with a sandwich ConvNeXt-based pre/post-processor to accurately extract more compact latent representation.
  arXiv  Detail & Related papers  (2023-07-12T11:32:02Z)
• MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation [41.029441562130984]
  Two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images. Our proposed modulated VQGAN is able to greatly improve the reconstructed image quality as well as provide high-fidelity image generation.
  arXiv  Detail & Related papers  (2022-09-19T13:26:51Z)
• Neural Distributed Image Compression with Cross-Attention Feature Alignment [1.2234742322758418]
  We consider a pair of stereo images, which have overlapping fields of view, captured by a synchronized and calibrated pair of cameras. We assume that one image of the pair is to be compressed and transmitted, while the other image is available only at the decoder. In the proposed architecture, the encoder maps the input image to a latent space using a DNN, quantizes the latent representation, and compresses it losslessly using entropy coding.
  arXiv  Detail & Related papers  (2022-07-18T10:15:04Z)
• Neural Data-Dependent Transform for Learned Image Compression [72.86505042102155]
  We build a neural data-dependent transform and introduce a continuous online mode decision mechanism to jointly optimize the coding efficiency for each individual image. The experimental results show the effectiveness of the proposed neural-syntax design and the continuous online mode decision mechanism.
  arXiv  Detail & Related papers  (2022-03-09T14:56:48Z)
• Towards End-to-End Image Compression and Analysis with Transformers [99.50111380056043]
  We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. We aim to redesign the Vision Transformer (ViT) model to perform image classification from the compressed features and facilitate image compression with the long-term information from the Transformer. Experimental results demonstrate the effectiveness of the proposed model in both the image compression and the classification tasks.
  arXiv  Detail & Related papers  (2021-12-17T03:28:14Z)
• Masked Autoencoders Are Scalable Vision Learners [60.97703494764904]
  Masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. Coupling these two designs enables us to train large models efficiently and effectively.
  arXiv  Detail & Related papers  (2021-11-11T18:46:40Z)
• 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.