Updates the results page for to fix markdown formatting.

1) Links should go to github repo view.
2) Fix double horizontal rule
3) Fix links (github doesn't like extra whitespace in [name](url)).

PiperOrigin-RevId: 296308708
Change-Id: I9159ef7e6d3f991dcc044cce92341982a073311d

Author: minnend

README.md

@@ -247,7 +247,7 @@ the same steps, but the Docker image should not be necessary.
 
 We provide evaluation results for several image compression methods in terms of
 different metrics in different colorspaces. Please see the
-[results subdirectory](https://tensorflow.github.io/compression/results/readme/image_compression/README.md)
+[results subdirectory](https://github.com/tensorflow/compression/tree/master/results/image_compression)
 for more information.
 
 ## Authors

results/image_compression/README.md

@@ -9,14 +9,12 @@ evaluation data sets are covered.
 
 ### Table of Contents
 
-* [Image Compression Methods](#image_compression_methods)
-* [Quality Metrics](#quality_metrics)
-* [Data Sets for Evaluation](#data_sets_for_evaluation)
+* [Image Compression Methods](#image-compression-methods)
+* [Quality Metrics](#quality-metrics)
+* [Data Sets for Evaluation](#data-sets-for-evaluation)
 
 ## Image Compression Methods
 
---------------------------------------------------------------------------------
-
 ### Standard (Hand-Engineered) Codecs
 
 *   JPEG (4:2:0)
@@ -27,73 +25,60 @@ evaluation data sets are covered.
 
 ### Learning-based Methods
 
-1.   [Context-adaptive Entropy Model for End-to-end Optimized Image Compression]
-    (https://openreview.net/forum?id=HyxKIiAqYQ) \
-    Jooyoung Lee, Seunghyun Cho, and Seung-Kwon Beack \
+1.   [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://openreview.net/forum?id=HyxKIiAqYQ)\
+    Jooyoung Lee, Seunghyun Cho, and Seung-Kwon Beack\
     Int. Conf. on Learning Representations (ICLR) 2019
 
 2.  [Joint autoregressive and hierarchical priors for learned image
-    compression]
-    (https://arxiv.org/abs/1809.02736) \
-    David Minnen, Johannes Ballé, and George Toderici \
+    compression](https://arxiv.org/abs/1809.02736)\
+    David Minnen, Johannes Ballé, and George Toderici\
     Advances in Neural Information Processing Systems (NeurIPS) 2018
 
-3.  [Learning a Code-Space Predictor by Exploiting Intra-Image-Dependencies]
-    (http://bmvc2018.org/contents/papers/0491.pdf) \
-    Jan P. Klopp, Yu-Chiang Frank Wang, Shao-Yi Chien, and Liang-Gee Chen \
+3.  [Learning a Code-Space Predictor by Exploiting Intra-Image-Dependencies](http://bmvc2018.org/contents/papers/0491.pdf)\
+    Jan P. Klopp, Yu-Chiang Frank Wang, Shao-Yi Chien, and Liang-Gee Chen\
     British Machine Vision Conference (BMVC) 2018
 
-4.  [Variational Image Compression with a Scale Hyperprior]
-    (https://arxiv.org/abs/1802.01436) \
+4.  [Variational Image Compression with a Scale Hyperprior](https://arxiv.org/abs/1802.01436)\
     Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick
-    Johnston \
+    Johnston\
     Int. Conf. on Learning Representations (ICLR) 2018
 
 5.  [Image-dependent local entropy models for image compression with deep
-    networks]
-    (https://arxiv.org/abs/1805.12295) \
+    networks](https://arxiv.org/abs/1805.12295)\
     David Minnen, George Toderici, Saurabh Singh, Sung Jin Hwang, and Michele
-    Covell \
+    Covell\
     Int. Conf. on Image Processing (ICIP) 2018
 
 6.  [Improved Lossy Image Compression With Priming and Spatially Adaptive Bit
-    Rates for Recurrent Networks]
-    (https://arxiv.org/abs/1703.10114) \
+    Rates for Recurrent Networks](https://arxiv.org/abs/1703.10114)\
     Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh,
-    Troy Chinen, Sung Jin Hwang, Joel Shor, and George Toderici \
+    Troy Chinen, Sung Jin Hwang, Joel Shor, and George Toderici\
     IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2018
 
-7.  [Real-Time Adaptive Image Compression]
-    (https://arxiv.org/abs/1705.05823) \
-    Oren Rippel and Lubomir Bourdev \
+7.  [Real-Time Adaptive Image Compression](https://arxiv.org/abs/1705.05823)\
+    Oren Rippel and Lubomir Bourdev\
     International Conference on Machine Learning (ICML) 2017
 
-8.  [End-to-end Optimized Image Compression]
-    (https://arxiv.org/abs/1611.01704) \
-    Johannes Ballé, Valero Laparra, and Eero P. Simoncelli \
+8.  [End-to-end Optimized Image Compression](https://arxiv.org/abs/1611.01704)\
+    Johannes Ballé, Valero Laparra, and Eero P. Simoncelli\
     Int. Conf. on Learning Representations (ICLR) 2017
 
-9.  [Lossy Image Compression with Compressive Autoencoders]
-    (https://openreview.net/forum?id=rJiNwv9gg) \
-    Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár \
+9.  [Lossy Image Compression with Compressive Autoencoders](https://openreview.net/forum?id=rJiNwv9gg)\
+    Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár\
     Int. Conf. on Learning Representations (ICLR) 2017
 
-10. [Spatially adaptive image compression using a tiled deep network]
-    (https://arxiv.org/abs/1802.02629) \
+10. [Spatially adaptive image compression using a tiled deep network](https://arxiv.org/abs/1802.02629)\
     David Minnen, George Toderici, Michele Covell, Troy Chinen, Nick Johnston,
-    Joel Shor, Sung Jin Hwang, Damien Vincent, and Saurabh Singh \
+    Joel Shor, Sung Jin Hwang, Damien Vincent, and Saurabh Singh\
     Int. Conference on Image Processing (ICIP) 2017
 
-11. [Full Resolution Image Compression with Recurrent Neural Networks]
-    (https://arxiv.org/abs/1608.05148) \
+11. [Full Resolution Image Compression with Recurrent Neural Networks](https://arxiv.org/abs/1608.05148)\
     George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David
-    Minnen, Joel Shor, and Michele Covell \
+    Minnen, Joel Shor, and Michele Covell\
     IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017
 
 ## Quality Metrics
 
---------------------------------------------------------------------------------
-
 ### Peak Signal-to-Noise Ratio (PSNR)
 
 According to
@@ -145,18 +130,16 @@ luminance channel (`Y'` in `Y'CbCr`) or by using a 6:1:1 weighted average over
 
 ## Data Sets for Evaluation
 
---------------------------------------------------------------------------------
-
 ### Kodak
 
 The Kodak data set is a collection of 24 images with resolution 768x512 (or
 512x768). The images are available as PNG files here:
 [http://r0k.us/graphics/kodak](http://r0k.us/graphics/kodak)
 
     @misc{kodak,
-      title="Kodak Lossless True Color Image Suite ({PhotoCD PCD0992})",
-      author="Eastman Kodak",
-      url = {http://r0k.us/graphics/kodak},
+      title = "Kodak Lossless True Color Image Suite ({PhotoCD PCD0992})",
+      author = "Eastman Kodak",
+      url = "http://r0k.us/graphics/kodak",
     }
 
 ### Tecnick
@@ -166,9 +149,9 @@ here (511 MB):
 [https://sourceforge.net/projects/testimages/files/OLD/OLD_SAMPLING/testimages.zip](https://sourceforge.net/projects/testimages/files/OLD/OLD_SAMPLING/testimages.zip)
 
     @inproceedings{tecnick,
-      author = {N. Asuni and A. Giachetti},
-      title = {{TESTIMAGES}: A large-scale archive for testing visual devices and basic image processing algorithms {(SAMPLING 1200 RGB set)}},
-      year = {2014},
-      booktitle = {{STAG}: Smart Tools and Apps for Graphics}
-      url = {https://sourceforge.net/projects/testimages/files/OLD/OLD_SAMPLING/testimages.zip},
+      author = "N. Asuni and A. Giachetti",
+      title = "{TESTIMAGES}: A large-scale archive for testing visual devices and basic image processing algorithms {(SAMPLING 1200 RGB set)}",
+      year = "2014",
+      booktitle = "{STAG}: Smart Tools and Apps for Graphics",
+      url = "https://sourceforge.net/projects/testimages/files/OLD/OLD_SAMPLING/testimages.zip",
     }