SlowGuess/NeurIPS_2023_OCR huggingface dataset:
F Additional visualization of the compressed images and decoding variability visualization
Figure 8: Ground Truth
Figure 9: CDC  $\mathcal{X}_{\theta}(\rho = 0.9)$ , bpp=0.205
Figure 10: HiFiC bpp=0.207
Figure 11: Ground Truth
Figure 12: CDC  $\mathcal{X}_{\theta}(\rho = 0.9)$ , bpp=0.398
Figure 13: HiFiC bpp=0.456
Figure 14: We stochastically decode the same latent variable  $\mathbf{z}$  and  $\gamma = 0.8$  but different random seeds for  $\mathbf{x}_N\sim \mathcal{N}(\mathbf{0},\gamma^2\mathbf{I})$ . Different random seeds may yield low-level textural distinction.
G Visualizations of the Decoding Process
(a) A deterministic decoding scheme
(b) A stochastic decoding scheme
Figure 15: Comparing two decoding schemes for decoding the same image, this visualization illustrates the evolution of the texture variable  $\mathbf{x}_n$  at five different time steps ( $n = \{0\%N, 30\%N, 60\%N, 90\%N, 100\%N\}$ ) using a total of  $N$  decoding steps. In the deterministic decoding scheme, the model transforms a grayscale image into the actual reconstruction. Conversely, in the stochastic decoding scheme, noise is dynamically "denoised" during the process, resulting in a reconstruction that incorporates high-frequency details.
H Additional Rate-Distortion(Perception) Results
Figure 16: Rate-Distortion(Perception) for COCO dataset. We use 500 decoding steps for  $\epsilon_{\theta}$  model.
Figure 17: Rate-Distortion(Perception) for Tecnick dataset. We use 500 decoding steps for  $\epsilon_{\theta}$  model.
Figure 18: Rate-Distortion(Perception) for Kodak dataset. We use 500 decoding steps for  $\epsilon_{\theta}$  model.
Figure 19: Rate-Distortion(Perception) for DIV2K dataset. We use 500 decoding steps for  $\epsilon_{\theta}$  model.. The complete 16 metrics.