In recent years, there has been a growing demand for long-distance target detection, leading to an increased focus on dim and small target detection technology based on the infrared band. However, this technology is often plagued by false alarms caused by flickering pixels in the Infrared Focal Plane Array (IRFPA). The occurrence of flickering pixels is multifaceted, resulting from a combination of internal defects and external conditions. In practical applications of infrared systems, the detector's operating point is typically adjusted dynamically based on the target and scene, thereby altering the time-frequency characteristics of the flickering pixels. During the calibration process, a single operating point is insufficient to stimulate all flickering pixels, resulting in only a partial display of flickering pixels. Furthermore, gray-level image detection is affected by detector non-uniformity, making it difficult to detect flickering pixels with low responsivity and leading to instances of missed detection.Flickering pixels can be attributed to various internal defects, including defects in the readout integrated circuit, poor contact, uneven carrier concentration, impurities, crystal dislocation, and so on. Additionally, external stress variations also contribute to the occurrence of flickering pixels. The temporal characteristics of flickering pixels are jointly determined by internal defects and external stress conditions. The complex mechanisms of these defects and the variable external stress conditions result in the chaotic behavior in flickering pixels. By summarizing the causes and mechanisms of flickering pixels, four distinct categories have been identified based on their specific time-frequency characteristics: Class Ⅰ-Forward burst noise flickering pixel, Class Ⅱ-Opposite burst noise flickering pixel, Class Ⅲ-Step noise flickering pixel, and Class Ⅳ-1/f noise flickering pixel. These four categories exhibit different trends of variation under compound operating point conditions.The noise in Class I primarily originates from defect energy levels generated by semiconductor defects within the bandgap. This type of noise is highly sensitive to the system's operating state and increases with the PN junction's working current. The noise slowly increases with the increase of temporal noise at the operating point. On the other hand, Class Ⅱ noise refers to flickering pixels, which are a specific type of Random Telegraph Signal (RTS) noise. As the channel width in the PN junction increases, the impact of interface defects on the non-uniformity of carrier current diminishes, leading to increased noise as the working current of the PN junction decreases. The noise slowly decreases with the increase of temporal noise at the operating point. Class Ⅲ noise is generated under particular external stress conditions and has limited correlation with the PN junction's working current. It arises from the combined effect of internal defects and external stress, resulting in random step noise at specific operating points. In Class IV noise, poor crystal contact is the primary factor, influenced by the operational state. This type of noise is more pronounced at low frequencies, with an overall downward trend in its spectrum. Similar to Class I noise, the noise slowly increases with the increase of temporal noise at the operating point.Apart from typical flickering pixels, there are also flickering pixels with a mixture of various defects, displaying more complex characteristics. Due to these complexities, calibrating flickering pixels under a single operating point is challenging.In comparison, compound condition operating points provide more comprehensive excitation conditions, enabling a higher occurrence of flickering pixels. The calibration of flickering pixels is more efficient and allows for an increased detection rate under compound condition operating points. Additionally, energy images can correct image non-uniformity, while the response value remains unaffected by the integration time. Combining energy images with gray-level images helps overcome missed detections caused by detector non-uniformity. Therefore, a calibration method for flickering pixels is proposed to address the limitations of traditional calibration methods. This method employs twice average energy temporal noise and twice average gray-level temporal noise as thresholds to detect flickering pixels under compound condition operating points. The detection results are then merged to obtain the flickering pixels map.This article verifies the proposed method through experiments conducted using the HgCdTe IRFPA detector (320×256) and the French HGH company's surface source blackbody (DCN 1 000H4). The experimental results show that the average detection rate of flickering pixels at a single operating point has increased by 12.49%, while the overall detection rate of flickering pixels has improved by 9.41% compared to traditional methods. The calibration method effectively addresses the issues of insufficient excitation conditions for flickering pixels at a single operating point and miss detection of flickering pixels caused by detector non-uniformity, thereby improving the detection rate of flickering pixels.