KEYWORDS: Video, Motion estimation, Super resolution, Video compression, Image processing, Image enhancement, Image quality, Point spread functions, Electrical engineering, Digital signal processing
In this paper, a new technique for robust super-resolution (SR) from compressed video is presented. The proposed method exploits the differences between low-resolution images at the pixel level, in order to determine the usability of every pixel in the low-resolution images for SR enhancement. Only the pixels, from the lowresolution
images, that are determined to be usable, are included in the L2-norm minimization procedure. Three different usability criterions are proposed, maximum distance from the median - MDM, maximum distance from initial image - MDIM, and maximum distance from the SR estimate - MDSRE. The results obtained with real video sequences demonstrate superior quality of the resulting enhanced image in the presence of outliers and same quality without outliers when compared to existing L2-norm minimization techniques. At the same time, the proposed scheme produces sharper images as compared to L1-norm minimization techniques.
KEYWORDS: Video, Video surveillance, Video compression, Motion estimation, Super resolution, Signal to noise ratio, Receivers, Video processing, Video coding, Target detection
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.
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