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Exploiting Data Mining for Fast Inter Prediction Mode Decision in HEVC

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Abstract

HEVC has made significant progress in coding efficiency. Compared with the previous generation H.264/AVC video coding standard, HEVC can save 50% of code rate under the same visual quality. However, this drop in bit rate is accompanied by an increase in computational complexity, which poses a great challenge to the application and development of HEVC. After analyzing the great correlation between the partitioning mode of coding unit in the encoder and several parameters, the partitioning problem of the CU is modeled as a classification problem in data mining, and it is decided to use a decision tree to solve this classification problem. We implement the decision tree on the official software HM16.2 and test the algorithm on the test set. Experiments show that fast algorithm based on decision tree can improve the encoding speed effectively with little influence on the encoding performance.

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Authors and Affiliations

  1. Nanjing University, Nanjing, China

    Tengrui Shi & Jian Wang

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  1. Tengrui Shi
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  2. Jian Wang
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Correspondence to Jian Wang.

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Shi, T., Wang, J. Exploiting Data Mining for Fast Inter Prediction Mode Decision in HEVC. Mobile Netw Appl 27, 1092–1100 (2022). https://doi.org/10.1007/s11036-018-1174-0

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  • DOI: https://doi.org/10.1007/s11036-018-1174-0

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