DEEP LEARNING-BASED CAMERA SETTINGS OPTIMIZATION - Shanthi P; Bhosale, Karuna S; Bajaj, Shweta; Jawahire, Snehal Swapnil; Srishti, Pooja; Rajak, Shilpa Kumari (ShodhKosh: Journal of Visual and Performing Arts, 2026)
In this paper, a deep learning framework of automatic optimization of camera settings is described and can be used to enhance the quality of images and videos of various real-world scenes. Conventional camera control is based on manual-grasped heuristics or auto modes which tend to malfunction when subject to challenging lighting, motion and texture changes. To overcome these limitations, the proposed method develops camera parameter tuning as a supervised learning problem which is a direct translation of scene characteristics to optimum exposure, ISO, aperture and focus settings. A cohesive neural design combines the convolutional feature learning of visual sensory input with auxiliary sensor information, which facilitates the understanding of the scene in dynamic settings. Multi-task learning is used to predict simultaneously a combination of several camera parameters that brings forward shared representations and maintains sensitivity to parameters. The architecture is trained and tested with heterogeneous image and video data with indoor and outdoor scenes, low-light environments, high dynamic range conditions and scenarios with high motion. The findings of experiments show that there are a steady enhancement of visual quality metrics, such as exposure accuracy, noise reduction, sharpness and color fidelity, compared to a conventional auto-camera pipelines. The analysis also demonstrates how the model is flexible towards unseen scene and can be deployed in real time due to light weight architecture design. Although such benefits exist, there are still issues of dataset bias, interpretability, and energy efficiency.