Abstract
Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed images usually have different levels of quality. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels, however, they may perform poorly when quality differences of the compressed images are quite subtle. To better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose an FR-IQA metric for fine-grained compressed images, which estimates the image quality by analyzing the difference of structure and texture. Our metric is mainly validated on the fine-grained compression IQA (FGIQA) database and is tested on other commonly used compression IQA databases as well. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the fine-grained compression IQA database and also obtains competitive performance on the coarse-grained compression IQA databases.
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