A new approach to video compression by refining the shortcomings of conventional approach and substituting each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole model is jointly optimized using a single loss function. Our work is based on a standard method to exploit the spatio-temporal redundancy in video frames to reduce the bit rate along with the minimization of distortions in decoded frames. We implement a neural network version of conventional video compression approach and encode the redundant frames with lower number of bit.
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MarcoRavich
commented
4 months ago
Hi there, 1st of all thanks for this awesome work !
Since we've 'doxed' it in our HyMPS project (under VIDEO section \ AI-based page \ Compression), can you please add a GH-compliant license file for it ?
As you know, expliciting licensing terms is extremely important to let anyone better/faster understand how to reuse/adapt/modify sources (and not only) in other open projects and vice-versa.
Although it may sounds like a minor aspect, license file omission obviously causes an inconsistent generation of the relative badge too:
You can easily set a standardized one through the GH's license wizard tool.
Last but not least, let us know how we could improve - in your opinion - our categorizations and links to resources in order to favor collaboration between developers (and therefore evolution) of listed projects.
Hope that helps/inspires !