Quality Assessment of End-to-End Learned Image Compression
Yang Li, Shiqi Wang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yue Wang
Abstract
Recently, learning-based lossy image compression has achieved notable breakthroughs with their excellent modeling and representation learning capabilities. Comparing to traditional image codecs based on block partitioning and transform, these data-driven approaches with artificial-neural-network (ANN) structures bring significantly different distortion patterns. Efficient objective image quality assessment (IQA) measures play the key role in quantitative evaluation and optimization of image compression algorithms. In this paper, we construct a large-scale image database for quality assessment of compressed images. In the proposed database, 100 reference images are compressed to different quality levels by 10 codecs, involving both traditional and learning-based codecs. Based on this database, we present a benchmark for existing IQA methods and reveal the challenges of IQA on learning-based compression distortions. Furthermore, we develop an objective quality assessment framework in which a self-attention module is adopted to leverage multi-level features from reference and compressed images. Extensive experiments demonstrate the superiority of our method in terms of prediction accuracy. The subjective and objective study of various compressed images also shed lights on the optimization of image compression methods.