Stable Diffusion is a Natural Cross-Modal Decoder for Layered AI-generated Image Compression

• URL: http://arxiv.org/abs/2412.12982v1
• Date: Tue, 17 Dec 2024 15:01:35 GMT
• Title: Stable Diffusion is a Natural Cross-Modal Decoder for Layered AI-generated Image Compression
• Authors: Ruijie Chen, Qi Mao, Zhengxue Cheng,
• Abstract summary: We introduce a scalable cross-modal compression framework that incorporates multiple human-comprehensible modalities. Our framework encodes images into a layered bitstream consisting of a semantic layer that delivers high-level semantic information. Our method proficiently restores both semantic and visual details, competing against baseline approaches at extremely lows.
• Score: 7.643300240138419
• License:
• Abstract: Recent advances in Artificial Intelligence Generated Content (AIGC) have garnered significant interest, accompanied by an increasing need to transmit and compress the vast number of AI-generated images (AIGIs). However, there is a noticeable deficiency in research focused on compression methods for AIGIs. To address this critical gap, we introduce a scalable cross-modal compression framework that incorporates multiple human-comprehensible modalities, designed to efficiently capture and relay essential visual information for AIGIs. In particular, our framework encodes images into a layered bitstream consisting of a semantic layer that delivers high-level semantic information through text prompts; a structural layer that captures spatial details using edge or skeleton maps; and a texture layer that preserves local textures via a colormap. Utilizing Stable Diffusion as the backend, the framework effectively leverages these multimodal priors for image generation, effectively functioning as a decoder when these priors are encoded. Qualitative and quantitative results show that our method proficiently restores both semantic and visual details, competing against baseline approaches at extremely low bitrates ( <0.02 bpp). Additionally, our framework facilitates downstream editing applications without requiring full decoding, thereby paving a new direction for future research in AIGI compression.

Related papers

• Toward Scalable Image Feature Compression: A Content-Adaptive and Diffusion-Based Approach [44.03561901593423]
  This paper introduces a content-adaptive diffusion model for scalable image compression. The proposed method encodes fine textures through a diffusion process, enhancing perceptual quality. Experiments demonstrate the effectiveness of the proposed framework in both image reconstruction and downstream machine vision tasks.
  arXiv  Detail & Related papers  (2024-10-08T15:48:34Z)
• Map-Assisted Remote-Sensing Image Compression at Extremely Low Bitrates [47.47031054057152]
  Generative models have been explored to compress RS images into extremely low-bitrate streams. These generative models struggle to reconstruct visually plausible images due to the highly ill-posed nature of extremely low-bitrate image compression. We propose an image compression framework that utilizes a pre-trained diffusion model with powerful natural image priors to achieve high-realism reconstructions.
  arXiv  Detail & Related papers  (2024-09-03T14:29:54Z)
• 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)
• Neural Graphics Texture Compression Supporting Random Access [34.974631096947284]
  We introduce a novel approach to texture set compression that integrates traditional GPU texture representation and NIC techniques. We propose an asymmetric auto-encoder framework that employs a convolutional encoder to capture detailed information in a bottleneck-latent space. Experimental results demonstrate that this approach provides much better results than conventional texture compression.
  arXiv  Detail & Related papers  (2024-05-06T19:44:13Z)
• MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model [78.4051835615796]
  This paper proposes a method called Multimodal Image Semantic Compression. It consists of an LMM encoder for extracting the semantic information of the image, a map encoder to locate the region corresponding to the semantic, an image encoder generates an extremely compressed bitstream, and a decoder reconstructs the image based on the above information. It can achieve optimal consistency and perception results while saving perceptual 50%, which has strong potential applications in the next generation of storage and communication.
  arXiv  Detail & Related papers  (2024-02-26T17:11:11Z)
• Perceptual Image Compression with Cooperative Cross-Modal Side Information [53.356714177243745]
  We propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features.
  arXiv  Detail & Related papers  (2023-11-23T08:31:11Z)
• You Can Mask More For Extremely Low-Bitrate Image Compression [80.7692466922499]
  Learned image compression (LIC) methods have experienced significant progress during recent years. LIC methods fail to explicitly explore the image structure and texture components crucial for image compression. We present DA-Mask that samples visible patches based on the structure and texture of original images. We propose a simple yet effective masked compression model (MCM), the first framework that unifies LIC and LIC end-to-end for extremely low-bitrate compression.
  arXiv  Detail & Related papers  (2023-06-27T15:36:22Z)
• Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation [36.20575570779196]
  We exploit the fine-grained-to-abstract and lowlevel-to-high-level feature hierarchy for the latent space of diffusion models. The hierarchical latent space of HDAE inherently encodes different abstract levels of semantics and provides more comprehensive semantic representations. We demonstrate the effectiveness of our proposed approach with extensive experiments and applications on image reconstruction, style mixing, controllable, detail-preserving and disentangled image manipulation.
  arXiv  Detail & Related papers  (2023-04-24T05:35:59Z)
• 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)
• Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression [113.30411004622508]
  We show that a universal and collaborative visual information representation can be achieved in a hierarchical way. Based on the strong generative capability of deep neural networks, the gap between the base feature layer and enhancement layer is further filled with the feature level texture reconstruction. To improve the efficiency of the proposed framework, the base layer neural network is trained in a multi-task manner.
  arXiv  Detail & Related papers  (2020-04-21T14:32:49Z)

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.