This paper presents an enhanced methodology for Recur- rence Quantification Analysis (RQA) designed specifically for video analysis. By utilizing image quality metrics, with a focus on the Peak Signal-to-Noise Ratio (PSNR), we deter- mine meaningful values for the RQA threshold ε, a critical factor for successful image processing. Utilizing the False Nearest Neighbors (FNN) technique, we identify the opti- mal embedding dimension D for each patch within the video frames. Our approach produces a heatmap that visualizes temporal recurrence information for each video patch.