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US 12,112,463 B2Granted
Method, device and computer readable medium for intrinsic popularity evaluation and content compression based thereon
AssigneeCity University of Hong Kong
InventorsShiqi Wang, Kede Ma, Keyan Ding
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Abstract
The present application provides methods, devices and computer readable media for intrinsic popularity evaluation and content compression based thereon. In an embodiment, there is provided a method of intrinsic popularity evaluation. The method comprises: receiving an image from a social network; and determining an intrinsic popularity score for the image using a deep neural network (DNN) based intrinsic popularity assessment model.
Claims9
A method of content compression, the method comprising:
- compressing information with a variable compression rate corresponding to an intrinsic popularity score of a content of the information, wherein the content of the information comprises an image;
- and
- before the information is compressed, determining the intrinsic popularity score of the image using a deep neural network (DNN) based intrinsic popularity assessment model;
- wherein the compressing of the information comprises:
- classifying the intrinsic popularity score into a popularity level;
- and
- compressing the image with the variable compression rate, the variable compression rate corresponding to the popularity level, the variable compression rate being in a negative correlation to the corresponding intrinsic popularity score, wherein the image is compressed by using a DNN based autoencoder, the DNN based autoencoder comprising learnable weights, the learnable weights being learnt according to an objective function given by =r+λd, where r is a coding cost, d is a reconstruction error, and λ is a Lagrange multiplier for controlling rate-distortion trade-off, and wherein A is a monotonic increasing function of the popularity level such that more bits are allocated for more popular images to thereby enable the image to be compressed with the variable compression rate corresponding to the popularity level and in the negative correlation to the corresponding intrinsic popularity score.
4 more elements
A device for content compression, the device comprising:
- at least one processor configurable to allocate resources;
- and
- a memory including computer program code for execution by the at least one processor, wherein the computer program code instructs the at least one processor to allocate resources by:
- compressing information with a variable compression rate corresponding to an intrinsic popularity score of a content of the information, wherein the content of the information comprises an image;
- and
- before the information is compressed, determining the intrinsic popularity score of the image using a deep neural network (DNN) based intrinsic popularity assessment model;
- wherein the compressing of the information comprises:
- classifying the intrinsic popularity score into a popularity level;
- and
- compressing the image with the variable compression rate, the variable compression rate corresponding to the popularity level, the variable compression rate being in a negative correlation to the corresponding intrinsic popularity score, wherein the image is compressed by using a DNN based autoencoder, the DNN based autoencoder comprising learnable weights, the learnable weights being learnt according to an objective function given by =r+λd, where r is a coding cost, d is a reconstruction error, and λ is a Lagrange multiplier for controlling rate-distortion trade-off, and wherein λ is a monotonic increasing function of the popularity level such that more bits are allocated for more popular images to thereby enable the image to be compressed with the variable compression rate corresponding to the popularity level and in the negative correlation to the corresponding intrinsic popularity score.
7 more elements
Dependencies are inferred from the claim text.