Infrared (IR) imagery sequences are commonly used for detecting moving targets in the presence of evolving cloud clutter or background noise. This research focuses on slow-moving point targets that are less than one pixel in size, such as aircraft at long range from a sensor. Since transmitting IR imagery sequences to a base unit or storing them consumes considerable time and resources, a compression method that maintains the point target detection capabilities is highly desirable. In this work, we introduce a new parametric temporal compression that incorporates Gaussian fit and polynomial fit. We then proceed to spatial compression by spatially applying the lowest possible number of bits for representing each parameter over the parameters extracted by temporal compression, which is followed by bit encoding to achieve an end-to-end compression process of the sequence for data storage and transmission. We evaluate the proposed compression method using the variance estimation ratio score (VERS), which is a signal-to-noise ratio (SNR)-based measure for point target detection that scores each pixel and yields an SNR scores image. A high pixel score indicates that a target is suspected to traverse the pixel. From this score image we calculate the movie scores, which are found to be close to those of the original sequences. Furthermore, we present a new algorithm for automatic detection of the target tracks. This algorithm extracts the target location from the SNR scores image, which is acquired during the evaluation process, using Hough transform. This algorithm yields a similar detection probability (PD) and false alarm probability (PFA) of the compressed sequences and the original sequences. The parameters of the new parametric temporal compression successfully differentiate the targets from the background, yielding high PDs (above 83%) with low PFAs (below 0.043%) without the need to calculate pixel scores or to apply automatic detection of the target tracks.