Deep-learning-based compression method using frequency decomposition
AssigneeInterDigital VC Holdings, Inc.
InventorsHyomin Choi, Fabien Racape, Shahab Hamidi-Rad, Simon Feltman
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AssigneeInterDigital VC Holdings, Inc.
InventorsHyomin Choi, Fabien Racape, Shahab Hamidi-Rad, Simon Feltman
Estimating the term…
In one implementation, we propose an end-to-end image video compression method that decomposes the spatial frequencies of the input content into a partitioned latent representation. Decomposed frequencies in the latent space are analyzed and grouped into separate latent representation or separate tensors, each tensor being jointly optimized to be decoded independently one from another. Therefore, the decoder can independently decode the tensors in a scalable manner to progressively reconstruct the input. This method enables quality scalability by progressively transmitting individual latent representations of decomposed frequency data, separated in the produced latent space. Furthermore, the quality scalability of region of interest (ROI) is enabled by which the decoder takes only corresponding latent representations in the enhancement tensors as input together with latent representations already delivered to the decoder.
A method of video encoding, comprising:
A method of video decoding, comprising:
An apparatus, comprising one or more processors and at least one memory coupled to said one or more processors, wherein said one or more processors are configured to:
An apparatus, comprising one or more processors and at least one memory coupled to said one or more processors, wherein said one or more processors are configured to:
Dependencies are inferred from the claim text.