Efficient On-the-Fly Adaptive Bit Mapping for Accelerating Image Super-Resolution

Core Concepts

The core message of this paper is to introduce the first on-the-fly adaptive quantization framework that accelerates the processing time for image super-resolution from hours to seconds, while achieving competitive performance with previous adaptive quantization methods.

Abstract

The paper proposes an efficient on-the-fly adaptive bit mapping framework for image super-resolution (SR) that adapts the bit-widths for different images and layers during inference.
Key highlights:

The paper observes that the layer-wise and image-wise quantization sensitivity can be processed separately, which simplifies the bit allocation problem to two policies: one for image-wise adaptation and one for layer-wise adaptation.
The image-to-bit mapping module maps an input image to an image-wise bit adaptation factor based on the complexity of the image. The layer-to-bit mapping module determines the layer-wise adaptation factors based on the sensitivity of each layer to quantization.
The mapping modules are calibrated and fine-tuned using a small set of calibration images, without requiring the full training dataset of low-resolution (LR) and high-resolution (HR) pairs.
The proposed method achieves on-par performance with previous adaptive quantization methods that require extensive quantization-aware training, while accelerating the processing time by over 2000x.
Extensive experiments demonstrate the effectiveness of the proposed adaptive bit mapping framework compared to static quantization methods and previous adaptive quantization approaches.

Stats

The EDSR model consists of 16 residual blocks with 64 channel dimensions.
The RDN model is a more extensive SR network of scale 4.
The calibration dataset is built by randomly sampling 100 LR images from the DIV2K training dataset.

Quotes

"To this end, we introduce the first on-the-fly adaptive quantization framework that accelerates the processing time from hours to seconds."
"We achieve on-par performance with QAT-based methods with ×2000 less processing time, pushing adaptive quantization to a new frontier."

Key Insights Distilled From

AdaBM

by Cheeun Hong,... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03296.pdf

Deeper Inquiries

How can the proposed adaptive bit mapping framework be extended to other computer vision tasks beyond image super-resolution

The proposed adaptive bit mapping framework can be extended to other computer vision tasks beyond image super-resolution by adapting the concept of adaptive bit allocation based on input complexity and layer sensitivity. For tasks like object detection, semantic segmentation, or image classification, the framework can be modified to dynamically adjust the bit-widths of network layers based on the complexity of the input data and the sensitivity of each layer to quantization. This adaptive approach can help optimize the computational resources required for different parts of the network, leading to improved efficiency and performance across various computer vision tasks.

What are the potential limitations or drawbacks of the separate image-wise and layer-wise bit adaptation approach, and how could they be addressed

One potential limitation of the separate image-wise and layer-wise bit adaptation approach is the need for accurate calibration of the complexity thresholds and sensitivity measures. If these thresholds are not properly set, it may lead to suboptimal bit allocation and affect the overall performance of the network. To address this limitation, more sophisticated algorithms or machine learning models can be employed to automatically learn and adjust these thresholds during training. Additionally, incorporating feedback mechanisms that continuously update the thresholds based on the network's performance during inference can help improve the adaptability and robustness of the bit allocation process.

Can the calibration and fine-tuning process be further optimized to reduce the required number of calibration images or iterations

The calibration and fine-tuning process can be further optimized to reduce the required number of calibration images or iterations by leveraging techniques such as transfer learning or meta-learning. By pre-training the bit mapping modules on a larger and more diverse dataset, the framework can learn generalizable bit allocation policies that require fewer calibration images for fine-tuning. Additionally, techniques like active learning, where the model selects the most informative images for calibration, can help reduce the number of required calibration images while still achieving optimal performance. Furthermore, optimizing the calibration and fine-tuning algorithms to converge faster by exploring more efficient optimization strategies can also help reduce the overall training time and resource requirements.