QoE-driven Unsupervised Image Categorization for Optimized Web Delivery
Parvez Ahammad, Brian Kennedy, Padmapani Ganti, Hariharan Kolam · 2014
Due to rapid growth in the richness of web applications and the multitude of wireless devices on which web content can be consumed, optimizing the content delivery of dynamic web applications represents an important technical challenge. One of the keys to solving the wireless web performance puzzle is that of efficient image content delivery. Since the critical factor in image delivery is the end-user experience, we propose a simple quantitative metric for characterizing the Quality of Experience (QoE) for any given image that gets sent through a web delivery service (WDS) pipeline. This quantitative signature, termed VoQS (\emph{variation of quality signature}), allows any two arbitrary images to be compared in the context of web delivery performance. We then use VoQS in conjunction with an unsupervised learning algorithm to group similarly performing images into coherent groups for optimized web delivery. Using a large database of images compiled from multiple content providers and diverse device-optimized formats, we demonstrate that our approach allows large image databases to be efficiently parsed into coherent groups in a content-dependent and device-targeted manner for optimized image content delivery. Our approach significantly decreases the average bits per image that need to be delivered across large image databases (by 43\% in our experiments) while preserving the perceptual quality across the entire image database. We also discuss how such a categorization approach can be leveraged for real-time web delivery of novel image data.