On Accuracy Enhancement of No-Reference Error-Tolerability Testing for Images in Object Detection Applications Based on RGB Channel Characteristics

Video data is essential for performing computer vision applications. However, errors due to noise or soft errors can be significant, potentially rendering the system invalid. Therefore, detecting such errors is important for these applications. In a real-time video stream, there is no golden reference video data to examine video quality during the functional operation of video processing. To address this issue, the no-reference testing method provides a highly attractive solution. In this work, we propose a no-reference test method that extracts error information from the RGB channels in video data. The proposed method improves test accuracy and precision compared to the previous method designed for human vision when applied to object detection. Experimental results show that more than 90% test accuracy and precision can be achieved. The costs incurred by the proposed method are also discussed. Additionally, based on the results, we further discuss setting different criteria for dynamic and static backgrounds of the target video.