arXiv:2504.20370v1 Announce Type: new
Abstract: Bayer-patterned color filter array (CFA) has been the go-to solution for color image sensors. In augmented reality (AR), although color interpolation (i.e., demosaicing) of pre-demosaic RAW images facilitates a user-friendly rendering, it creates no benefits in offloaded DNN analytics but increases the image channels by 3 times inducing higher transmission overheads. The potential optimization in frame preprocessing of DNN offloading is yet to be investigated. To that end, we propose ABO, an adaptive RAW frame offloading framework that parallelizes demosaicing with DNN computation. Its contributions are three-fold: First, we design a configurable tile-wise RAW image neural codec to compress frame sizes while sustaining downstream DNN accuracy under bandwidth constraints. Second, based on content-aware tiles-in-frame selection and runtime bandwidth estimation, a dynamic transmission controller adaptively calibrates codec configurations to maximize the DNN accuracy. Third, we further optimize the system pipelining to achieve lower end-to-end frame processing latency and higher throughput. Through extensive evaluations on a prototype platform, ABO consistently achieves 40% more frame processing throughput and 30% less end-to-end latency while improving the DNN accuracy by up to 15% than SOTA baselines. It also exhibits improved robustness against dim lighting and motion blur situations.
Analysis: Adaptation and Optimization in RAW Frame Offloading for Augmented Reality
The article introduces a novel approach called ABO (Adaptive RAW frame offloading) for optimizing the preprocessing of RAW images in the context of augmented reality (AR). The authors highlight the limitations of the traditional color interpolation (demosaicing) technique in AR, which increases image channels and transmission overheads without providing any benefits in offloaded deep neural network (DNN) analytics. This motivates the need for a new framework that optimizes the preprocessing of RAW frames to enhance DNN accuracy, frame processing throughput, and end-to-end latency in AR applications.
The multidisciplinary nature of this research becomes evident as it combines concepts from various fields such as computer vision, image processing, multimedia information systems, and augmented reality. By addressing the specific challenges posed by color interpolation in AR, the proposed framework brings together techniques from image compression, neural codec design, bandwidth estimation, and system optimization. This interdisciplinary approach allows for a holistic solution that improves the performance of AR systems.
Relevance to Multimedia Information Systems
Within the field of multimedia information systems, this research contributes to the area of image processing and optimization techniques for efficient data transmission and preprocessing. By considering the unique requirements of AR applications, the authors propose a configurable tile-wise RAW image neural codec that compresses frame sizes while maintaining DNN accuracy. This not only reduces transmission overheads but also allows for efficient storage and processing of RAW frames in multimedia systems.
Additionally, the incorporation of content-aware tiles-in-frame selection and runtime bandwidth estimation in the dynamic transmission controller demonstrates the integration of intelligent decision-making mechanisms in multimedia information systems. These techniques leverage contextual information to dynamically adjust codec configurations and maximize DNN accuracy. The optimization of system pipelining further enhances frame processing latency and throughput, which are crucial factors for real-time multimedia systems.
Connection to Animation, Artificial Reality, Augmented Reality, and Virtual Realities
While the focus of this article is specifically on augmented reality, it is worth noting the connections between this research and other areas such as animation, artificial reality, and virtual realities. These domains often rely on similar underlying technologies and face similar challenges related to image processing, system optimization, and rendering.
For instance, the optimization of image preprocessing in Augmented Reality can also apply to Virtual Reality systems, where the efficient handling of high-resolution image data is essential for creating immersive experiences. Similarly, the concept of adaptive offloading and intelligent decision-making algorithms can be extended to animation and artificial reality systems, where real-time rendering and content adaptation play a crucial role.
In conclusion, this article presents a comprehensive framework, ABO, that addresses the limitations of color interpolation in AR and optimizes RAW frame preprocessing for enhanced DNN accuracy, frame processing throughput, and end-to-end latency. With its multidisciplinary approach and relevance to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, this research contributes to the advancement of various fields and lays the foundation for more efficient and immersive multimedia experiences in the future.