On Enhancing Error-Tolerability of Videos via Re-Encoding with Adaptive I-Frame Insertion

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.