Switchable Token-Specific Codebook Quantization For Face Image Compression
Yongbo Wang, Haonan Wang, Guodong Mu, Ruixin Zhang, Jiaqi Chen, Jingyun Zhang, Jun Wang, Yuan Xie, zhizhong zhang, Shouhong Ding
Codebook-based image compression has achieved lower bits per pixel (bpp) while maintaining high reconstruction quality. These approaches utilize a globally shared codebook to quantize and reconstruct each token, controlling the bpp by adjusting the number of tokens or the codebook size. However, these methods perform poorly on facial images at low bpp: reducing the number of tokens or the codebook size significantly degrades the recognition performance of reconstructed faces. By analyzing facial images, we observed that pictures sharing the same attributes (e.g., gender, age, race) often exhibit many common features. At the same time, different facial regions demonstrate distinct characteristics (e.g., nose vs. ear).Based on these observations, we propose a group-routed codebook quantization and reconstruction method. By recording the codebook group to which each token belongs with a small number of bits, our method can reduce the loss incurred when decreasing the size of each codebook group. This enables a larger total number of codebooks under a lower overall bpp, thereby enhancing the expressive power and improving reconstruction performance. On face recognition datasets, our method outperforms state-of-the-art approaches such as 87.56\% and 91.66\% at the same bpp (0.0234), and remains competitive even when the bpp is reduced to 0.0040 (66.02\%).