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TUTCRIS

Random Forest Oriented Fast QTBT Frame Partitioning

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
KustantajaIEEE
Sivut1837-1841
Sivumäärä5
ISBN (elektroninen)9781479981311
DOI - pysyväislinkit
TilaJulkaistu - 1 toukokuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia
Kesto: 12 toukokuuta 201917 toukokuuta 2019

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
MaaIso-Britannia
KaupunkiBrighton
Ajanjakso12/05/1917/05/19

Tiivistelmä

Block partition structure is a critical module in video coding scheme to achieve significant gap of compression performance. Under the exploration of future video coding standard by the Joint Video Exploration Team (JVET), named Versatile Video Coding (VVC), a new Quad Tree Binary Tree (QTBT) block partition structure has been introduced. In addition to the QT block partitioning defined by High Efficiency Video Coding (HEVC) standard, new horizontal and vertical BT partitions are enabled, which drastically increases the encoding time compared to HEVC. In this paper, we propose a fast QTBT partitioning scheme based on a Machine Learning approach. Complementary to techniques proposed in literature to reduce the complexity of HEVC Quad Tree (QT) partitioning, the propose solution uses Random Forest classifiers to determine for each block which partition modes between QT and BT is more likely to be selected. Using uncertainty zones of classifier decisions, the proposed complexity reduction technique is able to reduce in average by 30% the encoding time of JEM-v7.0 software in Random Access configuration with only 0.57% Bjontegaard Delta Rate (BD-BR) increase.

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