A Variational Auto-Encoder Approach for Image Transmission in Noisy Channel

Estiri, Amir Hossein; Sabramooz, Mohammad Reza; Banaei, Ali; Dehghan, Amir Hossein; Jamialahmadi, Benyamin; Siavoshani, Mahdi Jafari

doi:10.1109/ist50524.2020.9345813

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Perceptual Quality Optimization [44], [45] Robust and Adaptable JSCC [46]- [48] Lightweight and Reliable Comm. [49] D. Spectrum Sensing and Recovery…”

Section: Joint Source Channel Codingmentioning

confidence: 99%

“…These schemes leverage deep GMs to optimize a weighted sum of mean squared error and learn perceptual image patch similarity losses. Moreover, [45] investigates the performance of VAEs compared to standard AEs for the transmission of image data over Gaussian point-to-point wireless channels, demonstrating that VAEs are more robust to channel degradation than AEs. Additionally, the authors employ the structural similarity, a perception-based metric, to optimize neural networks for improved human perceptual quality of the reconstructed images at the receiver.…”

Section: Image Transmission and Perceptual Quality Optimizationmentioning

confidence: 99%

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At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Celik,

Eltawil

2024

IEEE Open J. Commun. Soc.

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As we transition from the 5G epoch, a new horizon beckons with the advent of 6G, seeking a profound fusion with novel communication paradigms and emerging technological trends, bringing once-futuristic visions to life along with added technical intricacies. Although analytical models lay the foundations and offer systematic insights, we have recently witnessed a noticeable surge in research suggesting machine learning (ML) and artificial intelligence (AI) can efficiently deal with complex problems by complementing or replacing model-based approaches. The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. With these appealing attributes, GenAI can replace or supplement DAI methods in various capacities.Accordingly, this combined tutorial-survey paper commences with preliminaries of 6G and wireless intelligence by outlining candidate 6G applications and services, presenting a taxonomy of state-of-theart DAI models, exemplifying prominent DAI use cases, and elucidating the multifaceted ways through which GenAI enhances DAI. Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, autoregressive GMs, to name a few. Contrary to the prevailing belief that GenAI is a nascent trend, our exhaustive review of approximately 120 technical papers demonstrates the scope of research across core wireless research areas, including 1) physical layer design; 2) network optimization, organization, and management; 3) network traffic analytics; 4) crosslayer network security; and 5) localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic communications, integrated sensing and communications, THz communications, extremely large antenna arrays, near-field communications, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the multifarious challenges ahead, suggesting potential strategies and promising remedies. Given its depth and breadth, we are confident that this tutorial-cum-survey will serve as a pivotal reference for researchers and professionals delving into this dynamic and promising domain.

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“…Perceptual Quality Optimization [44], [45] Robust and Adaptable JSCC [46]- [48] Lightweight and Reliable Comm. [49] D. Spectrum Sensing and Recovery…”

Section: Joint Source Channel Codingmentioning

confidence: 99%

Section: Image Transmission and Perceptual Quality Optimizationmentioning

confidence: 99%

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Celik,

Eltawil

2024

IEEE Open J. Commun. Soc.

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show abstract

“…employed metrics such as pixel wise loss and compared it to SSIM under supervised learning neural network model 35 . reported perceptual image coding based on human vision system for transmission over wireless channels 36 . reported the use of variational auto‐encoders for image transmission over noisy channel.…”

Section: Literature Surveymentioning

confidence: 99%

“…35 reported perceptual image coding based on human vision system for transmission over wireless channels. 36 reported the use of variational auto-encoders for image transmission over noisy channel. SSIM as perceptual quality metric was employed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of full reference quality metrics for image transmission over a MIMO OWC channel under varying turbulent conditions

Gill

Singh

et al. 2023

Int J Communication

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The integrated OWC-PON architecture has a potential to overcome the limitations of existent RF technology and eliminate the last mile bottleneck in geographically constrained access networks thus providing real sense broadband from core to user. However, phenomenon such as atmospheric turbulence causes performance degradation in such systems. In the dissemination of multimedia services, the performance is the ultimate quality as perceived by end user's visual senses. To ascertain this quality in quantitative terms, full reference quality metrics are employed for communication purposes. This paper deals with classification, evaluation and analysis of various image quality assessment metrics for image transmission over OWC channel under varying turbulent conditions. The performance enhancement in terms of these metrics is investigated by deploying MIMO diversity reception. Moreover, a prediction model for SSIM is developed and compared for both SISO and MIMO links. The results show an improvement in IQA metrics with diversity reception. SSIM can be predicted in terms of log of refractive index structure parameter by a fourth order polynomial model for both SISO and MIMO receptions under all regimes of turbulence. The goodness of the fit statistics is presented along with the models to provide for their accuracy and reliability.

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Dynamic Facial Analysis by Common Space Variational Recurrent Deep Embedding

YUN

Hong-peng

2022

Fractals

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In human–machine interaction, non-verbal communication through the recognition of facial expressions is a process that operates on a time scale of the order of milliseconds, bringing a new dimension to how machines can affect modern social life. The ambiguity at this time scale is significant, necessitating that humans and machines rely on rich cognitive abilities rather than slow symbolic conclusions, making it necessary for humans and machines to rely on rich perceptual skills rather than sluggish figurative conclusions, especially in critical applications such as elderly care and supportive procedures for people with mobility or communication problems. This research field includes interdisciplinary contributions and specialized support from cognitive areas of psychology, sociology, linguistics, industrial design, and informatics. This paper presents an innovative emotion recognition system through dynamic facial analysis for optimal decision-making in non-verbal communication. It is an innovative model of artificial vision, for the recognition of emotions in the human–machine interaction, combining the look and the basic facial expressions of a person. This approach implements and proposes for the first time in the literature the application of a Common Space Variational Recurrent Deep Embedding (CSVRdE) intelligent learning system. The suggested technique simplifies the process of training customized extraction functions for appropriate image transformations in complicated neural network architectures, resulting in increased learning consistency, superior prediction reliability, and outstanding classification efficiency. Specifically, the suggested technique produces extremely accurate findings without reoccurring difficulties of unknown origin since all characteristics in the dataset are effectively managed.

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A Variational Auto-Encoder Approach for Image Transmission in Noisy Channel

Cited by 3 publications

References 11 publications

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Analysis of full reference quality metrics for image transmission over a MIMO OWC channel under varying turbulent conditions

Dynamic Facial Analysis by Common Space Variational Recurrent Deep Embedding

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