Objective

2

Given the development of digital video technology

especially the emergence of ultra-high definition (UHD) video technology

video compression faces enormous challenges. To solve the problem of voluminous data and to address the high-speed transmission requirements of UHD videos

the Joint Video Experts Team (JVET) is exploring future video coding (FVC) based on the high-efficiency video coding (HEVC) standard. FVC uses the hybrid coding framework of HEVC with new techniques. The compression efficiency of FVC is higher than that of HEVC; however

its coding complexity is extremely high. Therefore

reducing the complexity of FVC is of great significance. Among all the new techniques in FVC

the most effective but extremely time consuming one is the quad tree plus binary tree (QTBT) coding structure

which includes four partition modes

namely

quad tree split

vertical split

horizontal split

and no-split. The final split of coding units (CUs) is decided after trying all the partition modes and calculating the rate distortion cost. Thus

the complexity of the QTBT is extremely high. The existing HEVC-based fast coding method is no longer suitable for FVC because the QTBT coding structure and the recent work about low-complexity encoding methods are insufficient for FVC applications. To reduce the high complexity of FVC

the complexity of the QTBT structure should be considered. The traversal process of CU partition modes exhibits redundancy

and unnecessary attempts to achieve mode partition should be avoided. To optimize CUs' split process

we propose a random forest-based fast intra coding unit partition algorithm for FVC.

Method

2

The proposed algorithm is designed to optimize the QTBT structure in FVC. Compared with traditional statistical-based methods

the machine learning-based approach is more applicable because of the elaborate split modes of the QTBT structure. Among the methods of machine learning

random forest offers unique advantages. Random forest can handle the classification problem of multi-dimensional data and is strongly resistant to over-fitting and estimation. Furthermore

the approach performs well on classification issues and is suitable for CU splitting. Therefore

a fast algorithm based on random forest is proposed. The problem of distinguishing different split results of CUs is considered a classification problem

and random forest is used as the classifier. The image texture features and split results of the CU in the first frame of video sequences are first extracted. Image texture features have a strong correlation with split results and can thus be selected as the training data of the model. Various image texture features are used in the algorithm to achieve superior performance

and they are carefully selected by the calculation of feature importance. Specifically

the features finally used in the proposed algorithm are the width and height of the CU

Haar wavelet coefficients

angular second moment

entropy

contrast

inverse differential moment

and standard deviation. After the data collection process

four random forest models are established for different depths of CUs. CU depth can be represented as the joint depth of the quad tree and the binary tree

and this representative method is used to collect data in the algorithm. Then

the texture features and split results are set as multidimensional data

and they are separately trained online for each model. The training time is included in the entire encoding time and is relatively shorter than the encoding time. Finally

the trained models are used to predict the split results of the CUs of the remaining frames of the video sequences

thereby reducing the traversal of the partition modes and the time of rate distortion cost calculation. To ensure the algorithm's effectiveness

we test the accuracy of the models online by using different video sequences. The algorithm is implemented on the recently released JEM5.0 platform. A total of 22 test sequences of different contents and resolutions from class A1 to class E are tested under the common test condition

which is a full I-frame configuration mode with quantization parameters 22

27

32

and 37. The encoding performance of the algorithm is evaluated using the Bjontegaard delta bitrate (BDBR) and average amount of time saved between the proposed algorithm and the original platform.

Result

2

Experimental results show that compared with the original platform's algorithm

the proposed algorithm can decrease the average encoding time by 44.1% with negligible coding performance loss

and the BDBR only increases by 2.6%. The approach can also save more than 20% of encoding time relative to state-of-the-art methods

with BDBR slightly increasing. This algorithm is suitable for various classes of video sequences with different resolutions and textures. Among all the sequences

the sequences with high resolution save more encoding time than other sequences do because of the online training time consumption. Furthermore

the coding performance of the proposed algorithm is stable

thereby proving the effectiveness of the models.

Conclusion

2

A random forest-based fast intra CU partition algorithm for FVC is proposed to reduce the complexity of the QTBT structure in FVC. By extracting the texture features of images

the algorithm establishes random forest models to predict the CU partitioning result while avoiding the unnecessary traversal of split modes to save encoding time. The proposed intra prediction coding algorithm can effectively reduce the complexity of FVC and maintain the encoding performance. The proposed algorithm is more suitable for video sequences with high resolution. Furthermore

the proposed algorithm should be optimized in the future to enhance time reduction and reduce coding performance loss. The possibilities of machine learning in FVC inter-prediction will also be explored in the future.