Fast prediction mode selection and CU partition for HEVC intra coding

Fu, Bin; Zhang, Qiangqing; Hu, Jian

doi:10.1049/iet-ipr.2019.0259

Cited by 11 publications

(11 citation statements)

References 26 publications

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“…Fu et al. [16] proposed a dual support vector machine (SVM) based on texture features of CU and its sub‐CUs to select the size of CU. These texture features included direction complexity and content complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al [15] proposed a threshold-based texture classification using CNN, with joint consideration of the CU depth, quantization parameter, and texture complexity. Fu et al [16] proposed a dual support vector machine (SVM) based on texture features of CU and its sub-CUs to select the size of CU. These texture features included direction complexity and content complexity.…”

Section: Fast Algorithms For Hevcmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

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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“…Encoder compress the video sequence and creates a compressed form of video bit streams which may be used for storage or transmission. The video encoder involves the steps such as (i) Partition the picture (frame) into multiple units namely, Prediction Unit (PU) [15], Coding Unit (CU), Coding Tree Unit (CTU) (ii) Perform inter and intra prediction by subtracting prediction from unit (iii) Transform and quantize the residual errors (iv) Encode the output of transform, predicted information and header information. Video decoder decompress the compressed bitstream of video sequences and reconstruct the frames.…”

Section: Figure 1 Video Compression Systemmentioning

confidence: 99%

State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

Megala¹

2021

TURCOMAT

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Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.

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“…Based on an ensemble of online and offline random forests classifiers, Tahir et al [10] proposed a systematic approach to reduce the computational complexity of HEVC. Besides, Fu et al [25] using a dual SVM to efficiently select the CU size. Moreover, by jointly utilizing naive bayesian and SVM, Huang et al [5] proposed a novel fast intra-coding algorithm for HEVC to improve the intra-encoding speed.…”

Section: Related Workmentioning

confidence: 99%

“…To further demonstrate the performance of our proposed algorithm, we compare it with three additional algorithms on five video sequences. These three algorithms are all proposed most recently, and they are Huang's algorithm [5], Liu's algorithm [9] and Fu's algorithm [25]. The BD-rate loss and the encoding complexity reduction are shown in Table 7.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

Peng

et al. 2020

Electronics

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High Efficiency Video Coding (HEVC) has achieved about 50% bit-rates saving compared with its predecessor H.264 standard, while the encoding complexity increases dramatically. Due to the introduction of more flexible partition structures and more optional prediction directions, HEVC takes a brute force approach to find the optimal partitioning result which is much more time consuming. Therefore, this paper proposes a bagged trees based fast approach (BTFA) and focuses on the coding unit (CU) size decision for HEVC intra-coding. First, several key features of a target CU are extracted for three-output classifiers. Then, to avoid feature extraction and prediction time over head, our approach is designed frame-wisely, and the procedure is applied parallel with the encoding process. Using the adaptive threshold determination algorithm, our approach achieves 42.04% time saving with negligible 0.92% Bit-Distortion (BD)-rate loss. Furthermore, in order to calculate the optimal thresholds to balance BD-rate loss and complexity reduction, the neural network based mathematical fitting is added to BTFA, which is called the advanced bagged trees based fast approach (ABTFA). Finally, experimental results show that ABTFA achieves 47.87% time saving with only 0.96% BD-rate loss, which outperforms other state-of-the-art approaches.

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Fast prediction mode selection and CU partition for HEVC intra coding

Cited by 11 publications

References 26 publications

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

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

State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

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