Frequency Aware Learned Image Compression Using Swin Transformer and Discrete Wavelet Transform

In recent years, learned image compression (LIC) has made significant improvement in coding efficiency. However, existing LIC methods often ignore the extraction of local features, especially for repetitive textures. Moreover, they focus on feature extraction and compression in the spatial domain, overlooking the potential of frequency information. To address them, we propose a frequency aware LIC network that combines Swin Transformer and discrete wavelet transform (DWT). The encoder and decoder of the proposed LIC network has a symmetric network structure based on Swin Transformer to maintain the global feature extraction capability and guarantee the local feature extraction. To enhance the frequency learning ability, we introduce a frequency-aware module based on DWT in the hyper-prior network that captures context in the low-frequency domain and jointly learns spatial and frequency features. Since the training for LIC networks requires a lot of computing resources and time costs, we design a training strategy based on knowledge transfer to significantly improve the convergence speed and stability of training. The proposed training strategy optimizes parameter adjustment during training by reducing the number of iterations required for model training and avoiding overfitting, thereby significantly reducing training time and ensuring coding efficiency. Experimental results show that the proposed method achieves average BD-rate gains of 3.72% and 14.78% over VVC intra coding (VTM 11.0) on JPEG AI, Kodak24 and CLIC datasets in terms of PSNR and MS-SSIM, respectively.