Traditional structural testing often overlooks the actual impact of hardware faults on system-level applications, especially in error-tolerant components like video decoders. This work presents an application-aware fault classification framework that integrates a miter-based architecture with an early-exit strategy, using object detection accuracy as the evaluation target. By monitoring the accumulation of output errors during video decoding, the framework identifies application-critical faults early-typically within the first six frames of a 300-frame sequence-based on a predefined error threshold. Experimental results show that this approach achieves 100% classification precision while reducing simulation time by over 98%.

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.

Image stitching technology can stitch images (video frames) from multiple cameras into a single panoramic image, allowing a single image to cover a larger field of view. Image(video) stitching technology has also been widely used in real-time video applications such as online conference, VR or even Advanced Driver Assistance Systems (ADAS). Reliability of this technology is therefore of great importance. In this work, for the first time, we address the issue of error detection of an image stitching system. We show that there are inherent error-tolerability in this system, and the detection focus should be the unacceptable errors that would result in poor stitching results. In this work we also investigate the acceptability evaluation of the stitching results. In particular, a no-reference error detection technique is proposed so that the detection of unacceptable errors can be achieved in an online manner without golden data for comparison. Our experimental results show that the detection (acceptability classification) accuracy of the proposed error detection technique achieves 98.2%. In addition, the incurred performance overhead of our technique is ignorable. There is only 0.4% increase on the stitching execution time.

Videos are expected to be widely used in IoT or AI applications. The quality of videos are thus crucial for the success of these technologies. However, noises during transmission of videos, or aging of video processing or storage related circuits may significantly degrade the quality of videos, making the video become unacceptable. In this work we investigate the issue of video quality (error-tolerability) enhancement. Different from the previous work that mainly focuses on noisy videos, this work considers erroneous videos generated by faulty circuits. Single stuck-at faults are injected to an H.264 decoding circuit for generating erroneous videos. We find that adaptive insertion of I-frames is an attractive solution. This method first examines the quality of the current videos and accordingly suggests re-encoding of future videos with a proper number of additional I-frames. An error-tolerability enhancement flow for videos is proposed, which integrates video quality grading and determination of the suggested number of I-frames to be inserted. Limitations and implementation of this flow are also discussed. Experimental results on a total of 81,412 erroneous videos show that more than 90% of unacceptable erroneous videos whose quality is within a specified range become acceptable by applying the proposed flow.