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

摘要

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.