Weakly Supervised Learning for Blind Image Quality Assessment The blind image quality assessment (BIQA) metric based on deep neural network (DNN) achieves the best evaluation accuracy at present, and the depth of neural networks plays a crucial role for deep learningbased BIQA metric. However, training a DNN for quality assessment is known to be hard because of the lack of labeled data, and getting quality labels for a large number of images is very time consuming and costly. Therefore, training a deep BIQA metric directly will lead to overfitting in all likelihood. In order to solve this problem, we introduced a weakly supervised approach for learning a deep BIQA metric. First, we pretrained a novel encoderdecoder architecture by using the training data with weak quality annotations. The annotation is the error map between the distorted image and its undistorted version, which can roughly describes the distribution of distortion and can be easily acquired for training. Next, we finetuned the pretrained encoder on the quality labeled data set. Moreover, we used the group convolution to reduce the parameters of the proposed metric and further reduce the risk of overfitting. These training strategies, which reducing the risk of overfitting, enable us to build a very deep neural network for BIQA to have a better performance. Experimental results showed that the proposed model had the stateofart performance for various images with different distortion types.

Perceptual Image Quality Assessment: Recent Progress and Trends Image quality assessment (IQA) is useful in many visual processing systems but challenging to perform in line with the human perception. A great deal of recent research effort has been directed towards IQA. In order to overcome the difficulty and infeasibility of subjective tests in many situations, the aim of such effort is to assess visual quality objectively towards better alignment with the perception of the Human Visual system (HVS). In this work, we review and analyze the recent progress in the areas related to IQA, as well as giving our views whenever possible. Following the recent trends, we discuss the engineering approach in more details, explore the related aspects for feature pooling, and present a case study with machine learning.