Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks

TL;DR

This paper introduces a new benchmark and a unified framework for assessing the quality of burst images, aiming to improve downstream tasks like denoising and super-resolution by selecting high-quality frames.

Contribution

It establishes the first BuIQA benchmark dataset and proposes a task-driven, knowledge-distillation-based framework for versatile burst image quality assessment.

Findings

The benchmark dataset contains 7,346 burst sequences with annotated quality scores.

The proposed framework outperforms state-of-the-art methods across 10 downstream scenarios.

Applying BuIQA improves PSNR in denoising and super-resolution by 0.33 dB.

Abstract

In recent years, the development of burst imaging technology has improved the capture and processing capabilities of visual data, enabling a wide range of applications. However, the redundancy in burst images leads to the increased storage and transmission demands, as well as reduced efficiency of downstream tasks. To address this, we propose a new task of Burst Image Quality Assessment (BuIQA), to evaluate the task-driven quality of each frame within a burst sequence, providing reasonable cues for burst image selection. Specifically, we establish the first benchmark dataset for BuIQA, consisting of $7,346$ burst sequences with $45,827$ images and $191,572$ annotated quality scores for multiple downstream scenarios. Inspired by the data analysis, a unified BuIQA framework is proposed to achieve an efficient adaption for BuIQA under diverse downstream scenarios. Specifically, a task-driven prompt generation network is developed with heterogeneous knowledge distillation, to learn the priors of the downstream task. Then, the task-aware quality assessment network is introduced to assess the burst image quality based on the task prompt. Extensive experiments across 10 downstream scenarios demonstrate the impressive BuIQA performance of the proposed approach, outperforming the state-of-the-art. Furthermore, it can achieve $0.33$ dB PSNR improvement in the downstream tasks of denoising and super-resolution, by applying our approach to select the high-quality burst frames.