In this thesis, we propose a generic human attention region-of-interest (Generic-HAROI) algorithm to improve video compression while preserving subjective quality. Precisely, this algorithm performs a perceptual adaptive quantization algorithm on video frames as a function of the distribution of their luminance, motion vector, and color saturation. Our research incorporates a psycho-visual study that demonstrated that human attention automatically enhanced perceived saturation. As a result, the adaptive quantization phase of our compression algorithm is characterized by a luminance and saturation-aware just noticeable distortion (JND) function. After running multiple experiments on 18 videos with various resolutions ranging from QCIF to 4K, results showed that our method achieves higher compression than that of both the H.264/AVC JM and the HEVC HM while maintaining subjective quality. We observed that in comparison to both implementation of the standards (JM and HM), for an IPPP coding structure, the performance of our algorithm culminated with HD and 4K videos yielding a bit rate reduction averaging 15% and an encoding time reduction of about 20% in certain cases. Finally, after comparing our method to other similar techniques, we concluded that saturation is a significant parameter in the improvement of video compression.