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Rate-constrained learning-based image compression
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AI
DOI：10.1016/j.image.2021.116544.png)
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Rate control is a desirable feature, sometimes a requirement, for several applications in still image coding. Usually, the objective is to achieve rate control for every input data with minimal impact on rate-distortion performance. However, this task can be quite challenging. Learning-based image compression is a new paradigm that needs to be competitive with conventional image coding techniques. A learning-based lossy codec may require several trained models for different quality requirements. Therefore, a coding tool providing the ability to achieve a specific rate can be a deterministic factor to apply such models in practical application scenarios. Hence, in this work, we present a non-constrained solution to solve the constrained problem of training a learning-based image codec for a specific bitrate. The proposed solution requires a modified loss function for autoencoder optimization. This modification allows controlling the deviation from the specified target rate. Experiments performed in Kodak and JPEG AI datasets show that autoencoders trained with the proposed loss function can achieve rate constrained encoding with negligible losses in terms of SSIM and MS-SSIM.
Keyword:
Image coding
Neural networks
Rate-distortion
Rate control
Learning-based compression
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