Fast Decsion Tree based Inter Partitioning of VVC (PCS 2021)

This is the reproduction of paper Fast Versatile Video Coding using Specialised Decsion Trees [1] by Gosala Kulupana et al. published in Picture Coding Symposium 2021. Many thanks to Mr kulupana for sharing the source code of his implementation in VTM8 with me. This serves as the stat of art comparaison in our paper CNN-based Prediction of Partition Path for VVC Fast Inter Partitioning Using Motion Fields [2] currently under review of IEEE Transaction of Image Processing.

Comparing to original paper, we have generated a new dataset and trained the decision tree classifers. Then the proposed method and trained decision trees are integrated into VTM10. With the approvement of Kulupana, we share the dataset, trained decision tree, and all the related code in this repository. Python scripts for processing the raw data and training && pruning the decision tree are included in folder scripts. The master branch is our reproduction of the paper while the branch original_implementation contains the souce code in VTM8 offered by the author.

Build instructions

It is generally suggested to build 64-bit binaries for VTM software. We have built the software on Windows (MS Visual Studio) and Linux (make).

Windows Visual Studio 64 Bit:

Use the proper generator string for generating Visual Studio files, e.g. for VS 2019:

cd build
cmake .. -G "Visual Studio 16 2019" -A x64

Then open the generated solution file in MS Visual Studio.

For VS 2017 use "Visual Studio 15 2017 Win64", for VS 2019 use "Visual Studio 16 2019".

Visual Studio 2019 also allows you to open the CMake directory directly. Choose "File->Open->CMake" for this option.

For the release build of this project in Visual Studio, we should set the EncoderApp as Startup Project. Beforing building it, right click on the EncoderLib and set the "Treat Warnings As Error" to No in "Property->C/C++/General". Then add the compile option /bigobj in "Property->C/C++/Command Line" for EncoderLib.

Linux

For generating Linux Release Makefile:

cd build
cmake .. -DCMAKE_BUILD_TYPE=Release

For generating Linux Debug Makefile:

cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug

Then type

make -j

For more details, refer to the CMake documentation: https://cmake.org/cmake/help/latest/

Dataset Generation

In the original paper, the dataset was constructed using a portion of the Common Test Condition (CTC) sequences, while the remaining sequences were used for evaluating the method. In our implementation, we chose to generate the dataset using the BVI-DVC database [3] and the Youtube UVG database [4] to assess performance on the full CTC. All sequences in these databases have been encoded. Data is selectively collected from certain inter frames of the encoded sequences. More precisely, we collect data from one frame every three frames for resolutions of 960x544. For other resolutions, we specifically collect data from frames with a POC (Picture Order Count) equal to 8, 16, 28, 42, 49. The class D is excluded from the generation of dataset and therefore the trained RF models performs poorly on 240p sequences in JVET CTC. However, this does't impact the overall result of CTC sequences.

To adjust the frames for data collection, please modify the frame_collect variable, which is defined in line 1551 and 2905 in the file EncModeCtrl.cpp. The collected features are stored in two generated csv files, namely split_cost_yuvname.csv and split_features_yuvname.csv. To generate the dataset, please run the encoding with the macro COLLECT_DATASET activated, which can be found in line 68 of the file TypeDef.h. After obtaining the csv files, you will need several Python scripts to process the collected data.

1. Firstly, the script sep_frames.py is executed to seperate the collected data by frames. For 4k resolution, the frame-level data is then split by rows after running the script sep_rows.py. Splitting the large data file facilitates the multiprocessing of files.

2. Then we execute the script feature_ext.py to extract the features for QT/MT decisions and for Horzontal/Vertical decisions, respectively, for each data file.

3. Finally the feature files are merged per CU size and per decision. An equal number of samples for each decision has been randomly selected. In this stage, we begin by running sep_cu_sizes.py followed by the execution of the merge_features.py script.

The scripts for the above steps are provided in the scripts/processing folder. Finally, a dataset containing 21 numpy files is generated. If you are looking for details about the data format in this dataset, please refer to the code and the original paper.

Here is our dataset: https://1drv.ms/f/s!Aoi4nbmFu71Hgn0gvFYstymDzaFN?e=o2g9km

Training of Random Forests

Each file in the dataset mentioned above can be used to train a random forest model. For example, we use hv_16_16.npy to train the random forest for deciding between horizontal and vertical splits. Consequently, we trained 21 random forests using the scikit-learn library. We use 40 esimators, a maximum depth of 20, and a minimum number of samples per split of 100 for training these models. The trained models are stored in pickle files. Since the size of random forest models depends on the number of training samples, we select a specific number of training samples for each random forest to produce models with nearly the same size as the original models provided by the author. The training of random forest models is performed by running rf_train_models_qm.py and rf_train_models_hv.py in the scripts/training folder. When these scripts are executed, predictions of each decision tree in the random forest on a test set are also generated and saved.

Integration of Decision Trees in VTM10

The scripts involved in this section are located in the scripts/integratio directory. In the original paper, a specialised tree selection algorithm is presented. This algorithm aims to find the best subset of decision trees in a random forest model to achieve the highest prediction accuracy. By reusing the prediction results of decision trees obtained in the previous part, we execute the script eval_rf_models.py to evaluate the performance of different subsets of decision trees. Finally, the execution of get_tree_num.py is necessary to determine the best subset of decision trees for each random forest model.

We use the sklearn-porter library to convert the trained random forest into C code, as demonstrated in convert_rf.sh. Then we copy and paste the C code into rfTrainHor.cpp and rfTrainHor.cpp as the definitions of these classifiers. At the same time, the indices of decision trees of the best subset are defined in the file rfTrain.h. To evaluate the performance of our reproduction, you should build the project with the macro COLLECT_DATASET deactivated. To adjust the level of acceleration, you should use the command-line option -thdt which sets a threshold for the prediction of decision trees.

For reusing the code in this project, please think about citing paper [1] and [2]. Thanks! If you have further questions, please contact me at [email protected].

[1] G. Kulupana, V.P. Kumar M, and S. Blasi. Fast versatile video coding using specialised decision trees. In 2021 Picture Coding Symposium (PCS), pages 1–5, 2021

[2] Y. Liu, M. Riviere, T. Guionnet, A. Roumy, and C.Guillemot. CNN-based Prediction of Partition Path for VVC Fast Inter Partitioning Using Motion Fields. ArXiv, abs/2310.13838. (cf. https://arxiv.org/abs/2310.13838)

[3] D. Ma, F. Zhang, and D.R. Bull. Bvi-dvc: A training database for deep video compression. IEEE Transactions on Multimedia, 24:3847–3858, 2021.

[4] Y. Wang, S. Inguva, and B. Adsumilli. Youtube ugc dataset for video compression research. In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pages 1–5. IEEE, 2019