Super-resolution video enhancement based on a constrained set of motion vectors

Modern video surveillance and target tracking applications utilize multiple cameras transmitting low-bit-rate video through channels of very limited bandwidth. The highly compressed video exhibits coding artifacts that can cause target detection and tracking procedures to fail. Thus, to lower the level of noise and retain the sharpness of the video frames, super-resolution techniques can be employed for video enhancement. In this paper, we propose an efficient super-resolution video enhancement scheme that is based on a constrained set of motion vectors. The proposed scheme computes the motion vectors using the original (uncompressed) video frames, and transmits only a small set of these vectors to the receiver. At the receiver, each pixel is assigned a motion vector from the constrained set to maximize the motion prediction performance. The size of the transmitted vector set is constrained to be less than 3% of the total coded bit stream. In the video enhancement process, an L2-norm minimization super-resolution procedure is applied. The proposed scheme is applied to enhance highly compressed, real-world video sequences. The results obtained show significant improvement in the visual quality of the video sequences, as well as in the performance of subsequent target detection and tracking procedures.