Variable-Rate Learned Image Compression with Multi-Objective
  Optimization and Quantization-Reconstruction Offsets
Fatih Kamisli Fabien Racape Hyomin Choi
Achieving successful variable bitrate compression with computationally simple
algorithms from a single end-to-end learned image or video compression model
remains a challenge. Many approaches have been proposed, including conditional
auto-encoders, channel-adaptive gains for the latent tensor or uniformly
quantizing all elements of the latent tensor. This paper follows the
traditional approach to vary a single quantization step size to perform uniform
quantization of all latent tensor elements. However, three modifications are
proposed to improve the variable rate compression performance. First, multi
objective optimization is used for (post) training. Second, a
quantization-reconstruction offset is introduced into the quantization
operation. Third, variable rate quantization is used also for the hyper latent.
All these modifications can be made on a pre-trained single-rate compression
model by performing post training. The algorithms are implemented into three
well-known image compression models and the achieved variable rate compression
results indicate negligible or minimal compression performance loss compared to
training multiple models. (Codes will be shared at
https://github.com/InterDigitalInc/CompressAI)