Most frequent mode for intra‐mode coding in video coding

Yoon, Yong-Uk; Park, Do-Hyeon; Kim, Jae-Gon; Lee, Jinho; Kang, Jungwon

doi:10.1049/el.2018.7452

Cited by 8 publications

(3 citation statements)

References 1 publication

(2 reference statements)

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“…Therefore, overall, our algorithm performed better. In [32], Yoon et al obtained the mode of the current PU block through the neighboring blocks and achieved good results. However, they only considered the correlation between the current block and its neighbors, not the correlation between the more distant blocks and the correlation between the reference pixels and the pixels to be predicted.…”

Section: Compression Performancementioning

confidence: 99%

“…In [31], Weng et al presented an L-shape-based iterative algorithm to improve intra prediction accuracy, and a residual median edge detection method was also proposed to address edge information. In [32], Yoon et al exploited the number of occurrences of the modes in the neighboring blocks to extend the Most Probable Mode (MPM). Wang et al [33] designed a Sample Adaptive Offset (SAO) acceleration method to reduce the complexity of VVC.…”

Section: Introductionmentioning

confidence: 99%

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Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction

Xiong

Ren

Sheriff

et al. 2022

Sensors

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The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of bitrate, would reduce the burden of the sensors and improve their performance. Hence, to further enhance the intra compression performance of VVC, we propose a fusion-based intra prediction algorithm in this paper. Specifically, to better predict areas with similar texture information, we propose a fusion-based adaptive template matching method, which directly takes the error between reference and objective templates into account. Furthermore, to better utilize the correlation between reference pixels and the pixels to be predicted, we propose a fusion-based linear prediction method, which can compensate for the deficiency of single linear prediction. We implemented our algorithm on top of the VVC Test Model (VTM) 9.1. When compared with the VVC, our proposed fusion-based algorithm saves a bitrate of 0.89%, 0.84%, and 0.90% on average for the Y, Cb, and Cr components, respectively. In addition, when compared with some other existing works, our algorithm showed superior performance in bitrate savings.

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Section: Compression Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction

Xiong

Ren

Sheriff

et al. 2022

Sensors

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show abstract

“…In addition, a decision tree was utilized to exploit multiple dynamic lists of intra mode signaling [30]. By investigating the occurrences of intra modes in the neighboring blocks, Most Frequent Mode (MFM) list [42] was derived to compete with the existing MPM list. To skip intra mode signaling and save coding bits, Xu et al [39] proposed a predictive coding scheme, in which the angular correlation in spatial domain was calculated with modulo-N arithmetic operations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

Zhu¹,

Zhang²,

Li³

et al. 2022

Preprint

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In intra coding, Rate Distortion Optimization (RDO) is performed to achieve the optimal intra mode from a pre-defined candidate list. The optimal intra mode is also required to be encoded and transmitted to the decoder side besides the residual signal, where lots of coding bits are consumed. To further improve the performance of intra coding in Versatile Video Coding (VVC), an intelligent intra mode derivation method is proposed in this paper, termed as Deep Learning based Intra Mode Derivation (DLIMD). In specific, the process of intra mode derivation is formulated as a multi-class classification task, which aims to skip the module of intra mode signaling for coding bits reduction. The architecture of DLIMD is developed to adapt to different quantization parameter settings and variable coding blocks including non-square ones, which are handled by one single trained model. Different from the existing deep learning based classification problems, the hand-crafted features are also fed into the intra mode derivation network besides the learned features from feature learning network. To compete with traditional method, one additional binary flag is utilized in the video codec to indicate the selected scheme with RDO. Extensive experimental results reveal that the proposed method can achieve 2.28%, 1.74%, and 2.18% bit rate reduction on average for Y, U, and V components on the platform of VVC test model, which outperforms the state-of-the-art works.CCS Concepts: • Computing methodologies → Image compression.

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A support vector machine based fast planar prediction mode decision algorithm for versatile video coding

Yao

Wang

et al. 2022

Multimed Tools Appl

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Most frequent mode for intra‐mode coding in video coding

Cited by 8 publications

References 1 publication

Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction

Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

A support vector machine based fast planar prediction mode decision algorithm for versatile video coding

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