Image compression method based on multi-scale feature coding

A multi-scale feature and image compression technology, which is applied in image coding, graphic image conversion, image data processing, etc., can solve the problems of damage to the fineness of complex features, waste of bit rate, etc., to save bit rate and reduce the degree of loss Effect

Active Publication Date: 2020-04-03
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology allows images captured on multiple cameras or sensors to have their own unique characteristics that match up well together without sacrificing quality. It also assigns codes based upon these properties better than just one level per pixel. By doing this, it saves resources while still capturing high-quality visual data accurately.

Problems solved by technology

The technical problem addressed by this patented technology relates to improving image compressing techniques that rely heavily upon depth-learning algorithms like CNNs or other types of artificial intelligence (Al) networks for accurate analysis of images without sacrificing detail when analyzed with simpler models than those used during training.

Method used

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  • Image compression method based on multi-scale feature coding

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Embodiment Construction

[0054] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the implementations shown and described in the drawings are only exemplary, intended to explain the principle and spirit of the present invention, rather than limit the scope of the present invention.

[0055] The embodiment of the present invention provides an image compression method based on multi-scale feature coding, such as figure 1 As shown, the following steps S1-S5 are included:

[0056] S1. Perform feature extraction on the input image to obtain image features.

[0057] In the embodiment of the present invention, feature extraction is performed on the input image through four sequentially connected down-sampling convolutional layers at the image encoding end to obtain image features. The size of the convolution kernel of each downsampling convolutional layer is 5×5, the step size is 2, and the activation functi

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Abstract

The invention discloses an image compression method based on multi-scale feature coding. The image compression method comprises the steps: obtaining a selection vector through the averaging of the absolute values of gradient spectrums of image features of a training set, and guiding different channel features to select the coding resolution through the selection vector; meanwhile, recovering the features of low-resolution coding at a decoding end through a super-resolution network; and finally recombining the features of low-resolution coding with the features of high-resolution coding to forma complete feature spectrum, and mapping the complete feature spectrum back to an original image. According to the image compression method, difference processing is carried out according to the characteristics of the image characteristics, and the characteristics easy to recover from the context information are transmitted at low resolution, so that the code rate is saved; and for complex fine features, high-resolution transmission is adopted, so that the loss degree is reduced.

Description

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Claims

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Application Information

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Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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