Image coding apparatus, probability model generating apparatus and image decoding apparatus
AssigneeFujitsu Limited
InventorsSihan Wen, Jing Zhou, Zhiming Tan
Life of this patent
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Abstract
Embodiments of this disclosure provide an image coding apparatus, a probability model generating apparatus and an image decoding apparatus. A processor is to perform feature extraction on an input image to obtain first feature maps of N channels; to perform feature extraction on the input image with a size of the input image being adjusted K times, to respectively obtain second feature maps of N channels; and to concatenate the first feature maps of the K×N channels with the second feature maps of K×N channels to output a concatenated feature maps of channels. Hence, features of images may be accurately extracted and more competitive latent representations may be obtained.
Claims8
An apparatus, comprising:
- a processor to couple to a memory and to,
- perform feature extraction on an input image to obtain first feature maps of N channels;
- perform feature extraction on the input image with a size of the input image being adjusted K times, to respectively obtain second feature maps of K×N channels;
- concatenate the first feature maps of the N channels with the second feature maps of K×N channels to output concatenated feature maps of channels;
- assign weights to the concatenated feature maps of channels;
- and
- perform down-dimension processing on the weighted concatenated feature maps of channels to obtain feature maps of M channels and output the feature maps of M channels.
An apparatus, comprising:
- a processor to couple to a memory and to,
- perform feature extraction on output of a hyper decoder to obtain multi-scale auxiliary information;
- concatenate a latent representation of an input image from an arithmetic decoder with the multi-scale auxiliary information;
- and
- decode output from the concatenator to obtain a reconstructed image of the input image.
An apparatus, comprising:
- a processor to couple to a memory and to,
- perform feature extraction on output of a hyper decoder to obtain multi-scale auxiliary information;
- obtain information indicating content-based prediction by taking a latent representation of an input image from a quantizer as input;
- and
- process the information indicating the content-based prediction and the multi-scale auxiliary information to obtain a predicted probability model,
- wherein to perform the feature extraction, the processor is to use dilated convolution kernels of different dilation ratios and identical numbers of channels to obtain the multi-scale auxiliary information.
Dependencies are inferred from the claim text.