As we navigate the complexities of the digital age, computational imaging has emerged as a pivotal discipline, integrating advanced algorithms and artificial intelligence to enhance image processing techniques. With the rapid proliferation of high-resolution imaging technologies, there is an increasing demand for efficient coding and representation techniques that can optimize storage, transmission, and analysis of visual data. This Collection aims to explore the intersection of computational imaging and AI, investigating how novel methodologies can transform the way we capture, process, and interpret images.

The significance of this research extends beyond technical advancements; it is crucial for various fields, including medical imaging, autonomous vehicles, and entertainment. Recent breakthroughs in machine learning and neural networks have enabled unprecedented levels of image enhancement and classification. Techniques such as deep learning-based compression and generative adversarial networks (GANs) have revolutionized how we approach image representation. As industries increasingly rely on image data, developing efficient coding techniques becomes essential for enhancing data throughput while minimizing resource consumption.

Looking ahead, the continued exploration of computational imaging holds immense potential for future innovations. We may witness the development of real-time image processing solutions that integrate seamlessly with augmented reality and virtual reality systems. Furthermore, as AI continues to evolve, we can expect smarter algorithms that not only compress and enhance images but also learn from user interactions, leading to more intuitive and personalized imaging experiences.

- Advanced compression techniques using deep learning

- Machine learning applications in medical imaging

- Efficient coding methods for high-resolution video

- Representation techniques in augmented and virtual reality

Researchers are invited to submit their work to this Collection, which aims to showcase innovative research that contributes to the advancements in computational imaging and efficient coding techniques.

The "Computational Imaging in the AI Era: Efficient Coding and Representation Techniques" Collection seeks contributions that explore the integration of artificial intelligence with computational imaging. This Collection welcomes research on advanced compression methods, machine learning applications in various imaging domains, and innovative representation techniques. By fostering collaboration and knowledge sharing, this Collection aims to advance the field of computational imaging and enhance our understanding of efficient coding in the digital age.

SDG 9: Industry, Innovation & Infrastructure

SDG 8: Decent Work and Economic Growth

The rapid advancement of artificial intelligence (AI) is reshaping the fields of visual media processing, introducing new paradigms for compression efficiency, real-time processing, and practical delivery. As the volume of visual data continues to grow exponentially, the need for efficient, low-latency and secure coding frameworks has become increasingly critical. Researchers are actively exploring innovative approaches that integrate AI with traditional coding methods, enabling multimedia content to be transmitted and stored efficiently while meeting practical constraints such as latency, computational complexity, robustness, and security/privacy. This Special Issue aims to showcase cutting-edge research in this dynamic area, spanning both theoretical developments and practical implementations.

The integration of AI in visual media processing has opened up new avenues to improve compression efficiency and strengthen robust delivery in visual communication pipelines. Recent advancements, such as AI-assisted compression, learning-based/hybrid coding architectures, and joint compression-encryption frameworks, have demonstrated promising gains in performance, reliability, and adaptability to heterogeneous devices and networks. Neural media formats (e.g., NeRF and 3D Gaussian Splatting) further motivate research on efficient streaming and rendering-aware processing, but remain one of several relevant directions within the broader scope of emerging visual media. Advances are expected in AI-driven coding techniques that enable real-time processing, scalable quality adaptation, and efficient end-to-end operation across devices and networks. We particularly welcome contributions that clarify trade-offs among compression efficiency, computational cost, latency, robustness, and trust requirements, supported by rigorous evaluation or practical deployment.

Topics of interest include (but are not limited to):

• AI-Assisted Image and Video Compression
• Learning-based codecs and hybrid video coding frameworks
• Low-latency / real-time visual coding and streaming (edge/embedded deployment)
• Scalable and adaptive coding for heterogeneous devices and networks
• Secure and privacy-preserving visual communication
• Compressed Sensing, sparse representations, and efficient signalling for Visual Media
• Lightweight/low-complexity coding for Edge and Embedded Devices
• Robust / error-resilient coding and rate–distortion optimization under constraints
• Joint Compression-Encryption Frameworks
• Neural scene representation and compression (e.g., NeRF, 3D Gaussian Splatting)
• Secure and efficient scene editing in neural rendering pipelines

We invite researchers to submit original contributions to this Special Issue on AI-enabled Secure and Efficient Coding and Delivery for Emerging Visual Media. Submissions may address new models, algorithms, architectures, or systems, especially those that clarify the trade-offs among compression efficiency, computational cost, latency, robustness, and security, and that demonstrate impact through rigorous evaluation or real-world applicability.

SDG 9 – Industry, Innovation and Infrastructure

SDG 11 – Sustainable Cities and Communities

SDG 12 – Responsible Consumption and Production

SDG 13 – Climate Action