Video encoding methods save motion vector fields locally in a buffer to predict the following frames, thus reducing the size of the error pattern that is needed to decode the frames. Besides, this so-called motion-compensated prediction further reduces noise by reducing the signal-to-noise-ratio (SNR), regardless of the bit rate. Prior art is a spatial approach that reduces edge artifacts from blocking. This invention combines the spatial approach with a temporal trajectory filter to reduce noise as well as the data rate. Noise is filtered along temporal trajectories for every pixel which has two-dimensional coordinates describing the motion of pixels from frame to frame. The algorithm used here is less efficient for sources with a high quantizer, therefore an automatic shutoff is implemented for quantizers above 45. With the help of this new in-loop filtering algorithm, the picture quality could be improved considerably. This was proofed by the inventors using the H.264/AVC codec. In this way it was possible to reach a bit rate reduction up to 12 % (the average being 4 %).
Video image processing is a highly competitive, and with the rise ML/AI based on the date of submission there is a strong possibility this technique could have superseded newer more novel solutions. In the highly lucrative defense and government surveillance industry it may be to prove if this solution is being used. Applications: This solution likely valuable in within an existing or even new wholly/mostly software (not ML/AI) based solution. Companies: Wholly software based video companies and Cloud based video service providers
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