A dataset and model for the readability assessment of transparent micro-LED displays in smart cockpit

In the increasingly advanced automotive smart cockpit, the requirements for display with high readability and pleasant viewing experiences under various viewing directions, lead to significant challenges in product manufacture and modification. The micro light-emitting diode (micro-LED) display which has outstanding features, such as low power consumption, wider color gamut, longer lifetime, and small chip size, makes it a perfect candidate to design next-generation immersion vehicle display. However, the wide range of in- and out-vehicle lighting conditions that these displays should be able to operate in, makes the design of the evaluation set-up even more challenging. In this paper, we investigate a novel simulation-based evaluation framework for transparent micro-LED displays. Specifically, we collect the first display readability dataset by conducting comprehensive subjective studies. Based on this, we propose a novel objective display readability assessment model, which is comprised of three branches that are designed to extract readability-related features including scene semantics, technical distortions, and salient screen regions. In the experiments, we evaluate various blind image quality assessment algorithms, including both handcrafted feature-based models and deep learning-based models, on the proposed display readability dataset. The results show the effectiveness of our proposed objective display readability evaluator that achieves better subjective consistency than other baselines. The ablation studies further demonstrate the effectiveness of the proposed multi-branch feature extraction strategy and the image pre-processing scheme to filter out readability irrelevant information.