Fast depth intra coding based on texture feature and spatio‐temporal correlation in 3D‐HEVC

Li, Tiansong; Yu, Li; Wang, Hongkui; Chen, Yamei

doi:10.1049/ipr2.12021

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…Ref. [19] proposed a fast algorithm for diminishing complexity based on texture features and spatiotemporal correlation, using a combination of pixel-based statistical methods and edge detection to establish a new texture complexity model, based on which CUs are categorized into smooth, texture, and edges. After that, an early termination of the CU partitioning algorithm for smoothing blocks was proposed aiming to eliminate superfluous CUs.…”

Section: Fast Algorithm Based On Heuristicmentioning

confidence: 99%

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Wang,

Zhang

2023

Electronics

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The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-occurrence matrix (GLCM) and Extra trees for the characteristics of the depth map in 3D video. In the first stage, we introduce an edge detection algorithm using GLCM to classify the CU in the depth map into smooth and complex edge blocks based on the extracted features. Subsequently, the extracted features from the CUs, classified as complex edge blocks in the first stage, are fed into the constructed Extra trees model to make a fast decision on the partition type of that CU and avoid calculating unnecessary rate-distortion cost. Experimental results show that the overall algorithm can effectively reduce the coding time by 36.27–51.98%, while the Bjøntegaard delta bit rate (BDBR) is only increased by 0.24% on average which is negligible, all reflecting the superior performance of our method. Moreover, our algorithm can effectively ensure video quality while saving much encoding time compared with other algorithms.

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Section: Fast Algorithm Based On Heuristicmentioning

confidence: 99%

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Wang,

Zhang

2023

Electronics

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“…Li et al. [13] proposed a fast depth intra coding scheme to reduce computation complexity using texture feature and spatio‐temporal correlation in 3D‐HEVC. CU block would be classified into smooth, texture or edge block according to its texture feature, and early termination was introduced to reduce compression complexity.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm used texture partition of CTUs to predict the depth, while using texture direction to reduce the number of candidate modes for the RDO process. Li et al [13] proposed a fast depth intra coding scheme to reduce computation complexity using texture feature and spatio-temporal correlation in 3D-HEVC. CU block would be classified into smooth, texture or edge block according to its texture feature, and early termination was introduced to reduce compression complexity.…”

Section: Fast Algorithms For Hevcmentioning

confidence: 99%

QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC

Peng

2022

IET Image Processing

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In the newest generation of video coding standard, Versatile Video Coding (VVC), a new technique called Quad Tree with nested Multi‐type Tree (QTMT) structure is introduced. QTMT significantly improves the coding efficiency, but the improvement in compression performance comes at the cost of drastically increased complexity. This paper proposes a fast intra partition algorithm using Lightweight Neural Network (LNN) to skip QTMT partition steps which are unlikely to be chosen as the best split modes. Specifically, five LNNs (for five QTMT split modes) are trained, using features that consider the characteristic of 360‐degree videos. The experimental results demonstrate that the proposed QTMT‐LNN can reduce the encoding time from 52.28% to 72.17% on average, with coding efficiency losses ranging from 0.71% to 1.78%. Compared to other fast algorithms for intra coding unit (CU) partition, the method outperforms the related works in terms of Bjontegaard delta bit‐rate (BDBR) and encoding time reduction.

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Fast depth intra coding based on texture feature and spatio‐temporal correlation in 3D‐HEVC

Cited by 2 publications

References 39 publications

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC

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