Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks
This work addresses storage, transmission, and efficiency issues in burst imaging for applications like photography and video processing, presenting a novel benchmark and framework, though it is incremental in building on existing quality assessment methods.
The paper tackles the problem of redundancy in burst images by introducing Burst Image Quality Assessment (BuIQA) to evaluate task-driven quality per frame, establishing a benchmark dataset with 7,346 sequences and 45,827 images, and proposing a unified framework that outperforms state-of-the-art methods and improves downstream tasks by 0.33 dB PSNR in denoising and super-resolution.
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.