An Algorithm for Detecting Frame Errors Based on RGB Histogram Oscillations in Video Streams

Video frame errors caused by data corruption, compression artifacts, or transmission noise can severely impact visual quality and automated analysis. This paper presents a lightweight and interpretable algorithm for detecting such errors using color histogram analysis. The method constructs and normalizes histograms across RGB channels, identifies the frequency of color oscillations, and classifies frames as normal or erroneous based on a minimal oscillation threshold. Experimental evaluations confirm that the approach is efficient, suitable for real-time applications, and effective in detecting visually corrupted frames.