Vision Transformer-based Semantic Communications With Importance-Aware Quantization

• URL: http://arxiv.org/abs/2412.06038v1
• Date: Sun, 08 Dec 2024 19:24:47 GMT
• Title: Vision Transformer-based Semantic Communications With Importance-Aware Quantization
• Authors: Joohyuk Park, Yongjeong Oh, Yongjune Kim, Yo-Seb Jeon,
• Abstract summary: This paper presents a vision transformer (ViT)-based semantic communication system with importance-aware quantization (IAQ) for wireless image transmission. We show that our IAQ framework outperforms conventional image compression methods in both error-free and realistic communication scenarios.
• Score: 13.328970689723096
• License:
• Abstract: Semantic communications provide significant performance gains over traditional communications by transmitting task-relevant semantic features through wireless channels. However, most existing studies rely on end-to-end (E2E) training of neural-type encoders and decoders to ensure effective transmission of these semantic features. To enable semantic communications without relying on E2E training, this paper presents a vision transformer (ViT)-based semantic communication system with importance-aware quantization (IAQ) for wireless image transmission. The core idea of the presented system is to leverage the attention scores of a pretrained ViT model to quantify the importance levels of image patches. Based on this idea, our IAQ framework assigns different quantization bits to image patches based on their importance levels. This is achieved by formulating a weighted quantization error minimization problem, where the weight is set to be an increasing function of the attention score. Then, an optimal incremental allocation method and a low-complexity water-filling method are devised to solve the formulated problem. Our framework is further extended for realistic digital communication systems by modifying the bit allocation problem and the corresponding allocation methods based on an equivalent binary symmetric channel (BSC) model. Simulations on single-view and multi-view image classification tasks show that our IAQ framework outperforms conventional image compression methods in both error-free and realistic communication scenarios.

Related papers

• Efficient Semantic Communication Through Transformer-Aided Compression [31.285983939625098]
  We introduce a channel-aware adaptive framework for semantic communication. By employing vision transformers, we interpret the attention mask as a measure of the semantic contents of the patches. Our method enhances communication efficiency by adapting the encoding resolution to the content's relevance.
  arXiv  Detail & Related papers  (2024-12-02T18:57:28Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency [59.15544887307901]
  Image semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission. Existing ISC systems based on joint source-channel coding face challenges in interpretability, operability, and compatibility. We propose a novel trustworthy ISC framework that employs Generative Artificial Intelligence (GenAI) for multiple downstream inference tasks.
  arXiv  Detail & Related papers  (2024-08-07T14:32:36Z)
• Visual Language Model based Cross-modal Semantic Communication Systems [42.321208020228894]
  We propose a novel Vision-Language Model-based Cross-modal Semantic Communication system. The VLM-CSC comprises three novel components. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system.
  arXiv  Detail & Related papers  (2024-05-06T08:59:16Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Reasoning with the Theory of Mind for Pragmatic Semantic Communication [62.87895431431273]
  A pragmatic semantic communication framework is proposed in this paper. It enables effective goal-oriented information sharing between two-intelligent agents. Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits.
  arXiv  Detail & Related papers  (2023-11-30T03:36:19Z)
• Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission [68.69108124451263]
  Federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices. Each link is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator. Channel state information-based multiple-input multiple-output transmission module designed to combat channel fading and noise.
  arXiv  Detail & Related papers  (2023-08-07T16:32:14Z)
• Vector Quantized Semantic Communication System [22.579525825992416]
  We develop a deep learning-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifically, we propose a CNN-based transceiver to extract multi-scale semantic features of images and introduce multi-scale semantic embedding spaces. We employ adversarial training to improve the quality of received images by introducing a PatchGAN discriminator.
  arXiv  Detail & Related papers  (2022-09-23T10:58:23Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• VTAMIQ: Transformers for Attention Modulated Image Quality Assessment [0.0]
  We propose a novel full-reference IQA method, Vision Transformer for Attention Modulated Image Quality (VTAMIQ) Our method achieves competitive or state-of-the-art performance on the existing IQA datasets. With large-scale pre-training for both classification and IQA tasks, VTAMIQ generalizes well to unseen sets of images and distortions.
  arXiv  Detail & Related papers  (2021-10-04T18:35:29Z)

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.