  Recent years have witnessed the rapid development of image storage and
transmission systems, in which image compression plays an important role.
Generally speaking, image compression algorithms are developed to ensure good
visual quality at limited bit rates. However, due to the different compression
optimization methods, the compressed images may have different levels of
quality, which needs to be evaluated quantificationally. Nowadays, the
mainstream full-reference (FR) metrics are effective to predict the quality of
compressed images at coarse-grained levels (the bit rates differences of
compressed images are obvious), however, they may perform poorly for
fine-grained compressed images whose bit rates differences are quite subtle.
Therefore, to better improve the Quality of Experience (QoE) and provide useful
guidance for compression algorithms, we propose a full-reference image quality
assessment (FR-IQA) method for compressed images of fine-grained levels.
Specifically, the reference images and compressed images are first converted to
$YCbCr$ color space. The gradient features are extracted from regions that are
sensitive to compression artifacts. Then we employ the Log-Gabor transformation
to further analyze the texture difference. Finally, the obtained features are
fused into a quality score. The proposed method is validated on the
fine-grained compression image quality assessment (FGIQA) database, which is
especially constructed for assessing the quality of compressed images with
close bit rates. The experimental results show that our metric outperforms
mainstream FR-IQA metrics on the FGIQA database. We also test our method on
other commonly used compression IQA databases and the results show that our
method obtains competitive performance on the coarse-grained compression IQA
databases as well.
