Blind Image Quality Assessment with Deep Learning: A Replicability Study and Its Reproducibility in Lifelogging

Abstract

The wide availability and small size of different types of sensors have allowed for the acquisition of a huge amount of data about a person’s life in real time. With these data, usually denoted as lifelog data, we can analyze and understand personal experiences and behaviors. Most of the lifelog research has explored the use of visual data. However, a considerable amount of these images or videos are affected by different types of degradation or noise due to the non-controlled acquisition process. Image Quality Assessment can plays an essential role in lifelog research to deal with these data. We present in this paper a twofold study on the topic of blind image quality assessment. On the one hand, we explore the replication of the training process of a state-of-the-art deep learning model for blind image quality assessment in the wild. On the other hand, we present evidence that blind image quality assessment is an important pre-processing step to be further explored in the context of information retrieval in lifelogging applications. We consider that our efforts have been successful in the replication of the model training process, achieving similar results of inference when compared to the original version, while acknowledging a fair number of assumptions that we had to consider. Moreover, these assumptions motivated an extensive additional analysis that led to significant insights on the influence of both batch size and loss functions when training deep learning models in this context. We include preliminary results of the replicated model on a lifelogging dataset, as a potential reproducibility aspect to be considered.

Adaptive Feature Fusion and Kernel-Based Regression Modeling to Improve Blind Image Quality Assessment

“…A recent utilization of deep learning methods in IQA research prompted the use of convolutional neural networks CNN to extract image features in order to highlight degradation regions in an image. One major issue arises that deep learning based IQA methods are usually trained on smaller and artificially induced image datasets [12,13]. Moreover, CNNs are also deployed to extract features of distorted images to replace the conventional approach of hand-crafted features for IQA research [1].…”