Concept-Aware Web Image Compression Based on Crowdsourced Salient Object Detection

• Published in: 2019 5th International Conference on Web Research (ICWR) pp. 221 - 227
• Main Authors: Moradi, Morteza, Bayat, Farhad, Charmi, Mostafa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: Concept-based Image interpretation; Crowdsourcing; Data compression; Image coding; Image retrieval; Object detection; Salient Object Detection; Search engines; Web Image Compression; Web Image Optimization
• DOI: 10.1109/ICWR.2019.8765247

Reduced output quality and being unaware of content are among major issues with traditional image compression techniques. Such issues cause some critical problems when it comes to quality-intensive applications, including object/face detection and recognition, Web-based image viewers and management systems, etc. On the other side, efficiency of Web-based image search engines and retrieval systems in terms of user experience and usability could be affected. In order to cope with these challenges, a novel image compression method is proposed that takes advantages of collective human cognitive intelligence to detect the salient object(s) based on the recognized key concept(s). Then, other less-important regions/objects will be subject to the safe compression. Such an approach, besides preserving semantic aspects of the images that will result in smart (concept-aware) compression, could provide some crowdsourced labels for more efficient indexing and annotating of images. In this regard, two birds could be beaten with one stone: compressing Web images with respect to their content/concept and annotating them with crowd-suggested labels. The experimental results as well as user acceptance evaluation proved the efficacy of the introduced method.