Dynamic threshold-based keyframe detection and its application in rate control
This study proposes a new dynamic threshold model to detect keyframes for the coding of a video sequence. The proposed detection threshold is content adaptive which dynamically updates its value at each frame by taking into consideration both the statistical properties of the video sequence and the resolution as well as the quantisation parameters. The content variation metric used in the proposed detection method utilises the information that is already generated by the encoder during the encoding process and thus it makes the detection process fast. By applying the proposed scheme to rate control, the sizes of group of pictures are now adapted to the video content and it removes the temporal redundancy among different frames better, which transforms to the improvements on coding efficiency. The experimental results have shown that the proposed method achieves peak signal-to-noise ratio (PSNR) improvement.