Patent ID: 11917154
Assignee: TENCENT AMERICA LLC
Field: Audio-visual technology (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 14:
15. A non-transitory computer-readable medium storing instructions for that, when executed by at least one processor for end-to-end neural image compression using deep reinforcement learning, cause the at least one processor to:
encode an input;
generate a plurality of encoded representations of the input;
generate a set of quantization keys, using a first neural network, based on a set of previous quantization states, wherein each quantization key in the set of quantization keys and each previous quantization state in the set of previous quantization states correspond to the plurality of encoded representations;
generate a set of dequantized numbers representing dequantized representations of the plurality of encoded representations, based on the set of quantization keys, using a second neural network,
wherein the first neural network that is used to generate the set of quantization keys and the second neural network that is used to generate the set of dequantized numbers are updated using a first updating function,
wherein the encoder neural network and the decoder neural network are updated using a second updating function,
wherein the first updating function is different from the second updating function, and
wherein the first neural network used to generate the set of quantization keys, the second neural network used to generate the set of dequantized numbers, an encoder neural network, and a decoder neural network are jointly learned; and

decode a reconstructed output, based on the set of dequantized numbers.