Impact of Blind Image Quality Assessment on the Retrieval of Lifelog Images

The use of personal lifelogs can be beneficial to improve the quality of our life, as they can serve as tools for memory augmentation or for providing support to people with memory issues. In visual lifelogs, data are captured by cameras in the form of images or videos. However, a considerable amount of these images or videos are affected by different types of distortions or noise due to the non-controlled acquisition process. This article addresses the use of Blind Image Quality Assessment algorithms as a pre-processing approach in the retrieval of lifelogging images. As the amount of lifelog images has increased over the last few years, it is fundamental to find solutions to filter images in a lifelog data collection. We evaluate the impact of a Blind Image Quality Assessment algorithm by performing different retrieval experiments through a lifelogging system named MEMORIA. The results are promising and show that our approach can reduce the amount of images to process and retrieve in a lifelog data collection without losing valuable information, and provide to the user the most valuable images. By excluding a considerable amount of images in the pre-processing stage of a lifelogging system, its performance can be increased by saving time and resources.