The present invention relates to a
machine vision encoding method and
system based on self-
supervised learning. The method includes the following steps: randomly sampling image information into sub-blocks, inputting the sub-blocks into a
backbone network head to extract and transform feature channels to obtain a first feature; transforming the first feature to obtain a feature in a low-dimensional space, adding uniform
noise to the feature in the low-dimensional space through a quantizer to obtain a quantized feature, and reconstructing a compressed feature to obtain a second feature; transforming the second feature to a low-dimensional space, adding uniform
noise to the feature in the low-dimensional space through a quantizer to reduce redundancy, extracting and encoding
side information, decoding the
side information, using a mixed
Gaussian entropy model to predict the probability distribution parameters and
bit rate of the second feature, and reconstructing the dimension of the encoded feature as a third feature; extracting and transforming the dimension of the third feature, extracting and weighting the
convolution feature to form a
heat map, obtaining valid positive samples through the
heat map, and obtaining an encoding result. Compared with the existing technology, the present invention has the advantages of low encoding complexity and high semantic reliability.