Rate-Distortion Optimized Post-Training Quantization
for Learned Image Compression

Abstract

Quantizing a floating-point neural network to its fixed-point representation is crucial for Learned Image Compression (LIC) because it assures the decoding consistency for interoperability and reduces space-time complexity for implementation. Existing solutions often have to retrain the network for model quantization which is time-consuming and impractical. This work suggests the use of Post-Training Quantization (PTQ) to process pretrained, off-the-shelf LIC models directly. We theoretically prove that minimizing the mean square error (MSE) of model parameters (e.g., weight, bias, and activation) in PTQ is sub-optimal for compression tasks and thus develop a novel Rate-Distortion (R-D) Optimized PTQ (RDO-PTQ) to best retain the compression performance. Given a LIC model, RDO-PTQ layer-wisely determines the quantization factors to transform the original 32-bit floating-point (FP32) parameters to the 8-bit fixed-point (INT8) precision, for which a tiny calibration image set is compressed in optimization to minimize R-D loss. Experiments reveal the outstanding efficiency of the proposed method on different LICs, showing the closest coding performance to their floating-point counterparts. And, our method is a lightweight and plug-and-play approach without any need for model retraining which is attractive to practitioners. Such an RDO-PTQ is a task-oriented PTQ scheme, which is then extended to quantize popular super-resolution and image classification models with negligible performance loss, further evidencing the generalization of our methodology.

Overview

• We propose a task-oriented PTQ used for quantizing the LIC model considering the rate and distortion metrics jointly e.g., RDO-PTQ, with which it just needs a few random image samples to reconstruct models without any model retraining.
• The optimization will take local layer quantization loss L_lq and global model task loss L_task jointly into consideration, including scaling factor optimization and rounding optimization.
• We adopt learnable parameters to control dynamic range, and using bias rescaling to implement requantization and bias quantization.
• This task-oriented PTQ can be used for image classification and super-resolution (SR) tasks

Learned Image Compression

The diverse channel distributions of the layer

The diverse channel distributions.

Because of diverse channel distribution of the layer, layer-wise quantization will degrade quantization seriously. Channel-wise quantization is a better choice.

Evaluations on Kodak and Tecnick

R-D Performance in 8-bit.

R-D Performance in 10-bit.

Image Classification

Super Resolution

Preprint Paper

                @article{shi2022rate,
                    title={Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression},
                    author={Shi, Junqi and Lu, Ming and Ma, Zhan},
                    journal={arXiv preprint arXiv:2211.02854},
                    year={2023}
                }