Feature/update doc (#361)

* Add vmaf logo; add link to survey.

* Change logo size.

* Change logo size.

* Update logo size.

* Update CHANGELOG; bump up version to 1.3.15.

CHANGELOG.md

@@ -1,5 +1,22 @@
 # Change Log
 
+## (9/8/2019) [1.3.15]
+
+**Fixed bugs:**
+- Fix a case when CPU cores > 128(MAX_NUM_THREADS) / 3 (#319).
+- Avoid dis-filtering ref when not needed, fix return type (#325).
+- Update name of file for failed dis_path fopen (#334).
+- A few compilation fixes (warnings and errors) (#326).
+- Bump up g++ version to 9 for travis (#352).
+- Use stat struct instead of ftell to retrieve the file size (#350).
+
+**New features:**
+- Write aggregate scores, exec FPS to json output.
+- Add support for python3 (#332).
+- Print progress in vmafossexec (#337).
+- Add VMAF logo.
+- Add link to report VMAF bad cases.
+
 ## (3/1/2019) [1.3.14]
 
 **Fixed bugs:**

README.md

@@ -1,16 +1,19 @@
 VMAF - Video Multi-Method Assessment Fusion
 ===================
+
 [![Build Status](https://travis-ci.org/Netflix/vmaf.svg?branch=master)](https://travis-ci.org/Netflix/vmaf) [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/68i57b8ssasttngg?svg=true)](https://ci.appveyor.com/project/li-zhi/vmaf)
 
 VMAF is a perceptual video quality assessment algorithm developed by Netflix. VMAF Development Kit (VDK) is a software package that contains the VMAF algorithm implementation, as well as a set of tools that allows a user to train and test a custom VMAF model. Read [this](https://medium.com/netflix-techblog/toward-a-practical-perceptual-video-quality-metric-653f208b9652) techblog post for an overview, or [this](https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51ee9ed12) post for the latest updates and tips for best practices.
 
+![vmaf logo](resource/images/vmaf_logo.jpg)
+
 ## News
 
+- (9/8/19) Added a [link to report VMAF bad cases](https://docs.google.com/forms/d/e/1FAIpQLSdJntNoBuucMSiYoK3SDWoY1QN0yiFAi5LyEXuOyXEWJbQBtQ/viewform?usp=sf_link). Over time, we have received feedbacks on when VMAF's prediction does not reflect the expected perceptual quality of videos, either they are corner cases where VMAF fails to cover, or new application scenarios which VMAF was not initially intended for. In response to that, we have created the Google form to allow users to upload their video samples and describe the scenarios. The bad cases are valuable for improving future versions of VMAF. Users can opt in or out for sharing their sample videos publicly.
 - (1/31/19) Optimized C code for speed. Running in multithreading mode, `vmafossexec` achieves ~40% run time reduction compared to the previous version.
 - (11/19/18) Added a BD-rate calculator implementation. See more details [here](resource/doc/VMAF_Python_library.md#bd-rate-calculator).
 - (10/25/18) We have published our [second techblog on VMAF](https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51ee9ed12), with recommendations on best practices.
 - (9/13/18) [SUREAL](https://github.com/Netflix/sureal) is no longer a submodule to VMAF.
-- (6/19/18) Each VMAF prediction score now comes with a 95% [confidence interval (CI)](resource/doc/conf_interval.md), which quantifies the level of confidence that the prediction lies within the interval.
 
 ## Frequently Asked Questions
 

VERSION

@@ -1,2 +1,2 @@
-VMAF Development Kit (VDK) Version 1.3.14
+VMAF Development Kit (VDK) Version 1.3.15
 VMAF Version 0.6.1

wrapper/libvmaf.pc

@@ -5,7 +5,7 @@ includedir=/usr/local/include
 
 Name: libvmaf
 Description: Netflix's VMAF library
-Version: 1.3.14
+Version: 1.3.15
 URL: https://github.com/Netflix/vmaf
 Requires:
 Requires.private:

wrapper/src/vmaf.cpp

@@ -948,7 +948,7 @@ void BootstrapVmafQualityRunner::_set_prediction_result(
 
 }
 
-static const char VMAFOSS_DOC_VERSION[] = "1.3.14";
+static const char VMAFOSS_DOC_VERSION[] = "1.3.15";
 
 double RunVmaf(const char* fmt, int width, int height,
                int (*read_frame)(float *ref_data, float *main_data, float *temp_data, int stride, void *user_data),