Update README.md

README.md

@@ -16,21 +16,20 @@ TerraCodec (TEC) is a family of pretrained neural compression codecs for **multi
 
 This repository provides **Temporal Transformer (TEC-TT) models trained on Sentinel-2 L1C imagery**. The main TerraCodec models are released for Sentinel-2 L2A data, the L1C variants were used for declouding experiments in the paper.
 
-![Reconstructions](assets/reconstructions.png)
-
+![Reconstructions](https://raw.githubusercontent.com/IBM/TerraCodec/main/assets/reconstructions.jpg)
 ---
 
 # Model Architecture
 
 This repository contains the **TEC-TT (Temporal Transformer)** variants of TerraCodec for **S2L1C data**.
 
-[![TEC-TT Architecture](assets/TEC_TT_architecture.png)](assets/TEC_TT_architecture.png)
+![assets/TEC_TT_architecture.png](https://raw.githubusercontent.com/IBM/TerraCodec/main/assets/TEC_TT_architecture.png)
 
 TEC-TT extends the TerraCodec image codecs by modeling temporal dependencies across satellite image sequences. Each frame is first encoded using an ELIC-style CNN encoder–decoder to obtain latent representations. A temporal transformer then predicts the probability distribution of the current frame’s latents conditioned on previously encoded frames.
 
 By exploiting redundancy across seasonal observations, TEC-TT achieves improved compression efficiency for multi-temporal satellite imagery.
 
-See the paper for additional architectural and training details.
+See the [paper](https://arxiv.org/abs/2510.12670) for additional architectural and training details.
 
 ---
 
@@ -96,8 +95,8 @@ GitHub repository: https://github.com/IBM/TerraCodec
 If you use TerraCodec in your research, please cite:
 ```
 @article{terracodec2025,
-  title   = {TerraCodec: Neural Codecs for Earth Observation},
-  author  = {Costa Watanabe, Julen and Wittmann, Isabelle and Blumenstiel, Benedikt},
+  title   = {TerraCodec: Compressing Optical Earth Observation Data},
+  author  = {Costa Watanabe, Julen and Wittmann, Isabelle and Blumenstiel, Benedikt and Schindler, Konrad},
   journal = {arXiv preprint arXiv:2510.12670},
   year    = {2025}
 }