Low-Complexity Overfitted Neural Image Codec
Résumé
We propose a neural image codec at reduced complexity which overfits the decoder parameters to each input image. While autoencoders perform up to a million multiplications per decoded pixel, the proposed approach only requires 2300 multiplications per pixel. Albeit low-complexity, the method rivals autoencoder performance and surpasses HEVC performance under various coding conditions. Additional lightweight modules and an improved training process provide a 14% rate reduction with respect to previous overfitted codecs, while offering a similar complexity. This work is made open-source at http://orange-opensource.github.io/Cool-Chic/.
Mots clés
Codec
Computer science
Pixel
Autoencoder
Coding (social sciences)
Computational complexity theory
Artificial intelligence
Decoding methods
Encoder
Image (mathematics)
Computer vision
Computer engineering
Artificial neural network
Algorithm
Computer hardware
Mathematics
Statistics
Operating system
Training
Codecs
Image coding
Conferences
Neural networks
Signal processing
Encoding
Domaines
Sciences de l'ingénieur [physics]
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