Abstract
Image Quality Assessment (IQA) is essential in various Computer Vision tasks such as image deblurring and super-resolution. However, most IQA methods require reference images, which are not always available. While there are some reference-free IQA metrics, they have limitations in simulating human perception and discerning subtle image quality variations. We hypothesize that the JPEG quality factor is representatives of image quality measurement, and a well-trained neural network can learn to accurately evaluate image quality without requiring a clean reference, as it can recognize image degradation artifacts based on prior knowledge. Thus, we developed a reference-free quality evaluation network, dubbed "Quality Factor (QF) Predictor", which does not require any reference. Our QF Predictor is a lightweight, fully convolutional network comprising seven layers. The model is trained in a self-supervised manner: it receives JPEG compressed image patch with a random QF as input, is trained to accurately predict the corresponding QF. We demonstrate the versatility of the model by applying it to various tasks. First, our QF Predictor can generalize to measure the severity of various image artifacts, such as Gaussian Blur and Gaussian noise. Second, we show that the QF Predictor can be trained to predict the undersampling rate of images reconstructed from Magnetic Resonance Imaging (MRI) data.
Abstract (translated)
图像质量评估（IQA）在各种计算机视觉任务中（如图像去噪和超分辨率）非常重要。然而，大多数IQA方法都需要参考图像，这些图像并不总是可用的。虽然有一些无需参考图像的IQA指标，但它们在模拟人类感知和辨别细微图像质量变化方面存在局限性。我们假设JPEG质量因子是图像质量测量的代表，并且经过良好训练的神经网络可以准确评估图像质量，而不需要干净的参考，因为它可以根据先验知识识别图像退化伪像。因此，我们开发了一个无需参考的图像质量评估网络，名为“质量因子（QF）预测器”，它包含七个层。该模型以自监督的方式训练：它接收经过随机QF的压缩JPEG图像补丁作为输入，并通过准确预测相应的QF进行训练。我们通过应用该模型到各种任务来展示其多才性。首先，我们的QF预测器可以推广用于衡量各种图像伪像的严重程度，如高斯平滑和高斯噪声。其次，我们证明了QF预测器可以被训练预测从磁共振成像（MRI）数据中重构的图像的降采样率。
URL
https://arxiv.org/abs/2405.02208
