  Image compression constitutes a significant challenge amidst the era of
information explosion. Recent studies employing deep learning methods have
demonstrated the superior performance of learning-based image compression
methods over traditional codecs. However, an inherent challenge associated with
these methods lies in their lack of interpretability. Following an analysis of
the varying degrees of compression degradation across different frequency
bands, we propose the end-to-end optimized image compression model facilitated
by the frequency-oriented transform. The proposed end-to-end image compression
model consists of four components: spatial sampling, frequency-oriented
transform, entropy estimation, and frequency-aware fusion. The
frequency-oriented transform separates the original image signal into distinct
frequency bands, aligning with the human-interpretable concept. Leveraging the
non-overlapping hypothesis, the model enables scalable coding through the
selective transmission of arbitrary frequency components. Extensive experiments
are conducted to demonstrate that our model outperforms all traditional codecs
including next-generation standard H.266/VVC on MS-SSIM metric. Moreover,
visual analysis tasks (i.e., object detection and semantic segmentation) are
conducted to verify the proposed compression method could preserve semantic
fidelity besides signal-level precision.
