Decoder-Side Intra Mode Derivation with Texture Analysis in VVC Test Model

Nasrallah, Anthony; Abdoli, Mohsen; Mora, Elie Gabriel; Guionnet, Thomas; Raulet, Mickaël

doi:10.1109/icip.2019.8803773

Cited by 3 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Narsallah's scheme [27], it reduces 0.19%, 0.26%, and 0.12% bit rate on average for Y, U, and V components, respectively. 0.45%, 0.34%, and 0.27% bit rates are saved for Y, U, and V components in the Abdoli's scheme [1].…”

Section: Coding Performance Comparisonmentioning

confidence: 99%

“…With the neighboring blocks and reference pixels, 73 hand-crafted features are collected, including sixty-seven features from gradient histogram, five features from intra mode of neighboring blocks, and QP value. The gradient histogram can be regarded as the probability of each candidate intra mode, and its detailed calculation can be found in [27]. Due to the highly spatial correlation, the Up-Left (UL), Up (U), Up-Right (UR), Left (L) and Bottom Left (BL) blocks are used to provide their final selected intra modes as five hand-crafted features, as shown in the module of hand-crafted feature collection in Fig.…”

Section: Deep Learning Based Intra Mode Derivationmentioning

confidence: 99%

“…Three state-of-the-art works are adopted for coding performance comparison. Narsallah's scheme [27] derives the intra mode with gradient histogram and the one with the highest probability is determined eventually. Abdoli's scheme [1] produces a new intra prediction result with weighted intra modes from the top-2 highest probability in gradient histogram.…”

Section: Coding Performance Comparisonmentioning

confidence: 99%

“…From this comparison, it can be observed that the proposed method is better than other three methods. Compared with Narsallah's scheme [27], the proposed method not only adopts the existing hand-crafted features, but also learns features in highly dimensional space for the intra mode derivation.…”

Section: Coding Performance Comparisonmentioning

confidence: 99%

“…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. Additionally, template based [38], histogram of gradients based [28], and texture analysis based [27] intra mode derivation methods were presented in a manual manner. Basically, the MPM list construction and intra mode derivation have been investigated by traditional statistics and experience, which still can be further improved with advanced learning based schemes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

Zhu¹,

Zhang²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Coding Performance Comparisonmentioning

confidence: 99%

Section: Deep Learning Based Intra Mode Derivationmentioning

confidence: 99%

Section: Coding Performance Comparisonmentioning

confidence: 99%

Section: Coding Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

Zhu¹,

Zhang²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

CCLM combined with decoder-side intra mode derivation

Lee

Kim

2023

International Workshop on Advanced Imaging Technology (IWAIT) 2023

View full text Add to dashboard Cite

The Cross Component Linear Model (CCLM) is an intra prediction tool for chroma that was adopted in Versatile Video Coding (VVC) standard. It generates an intra-predictor by utilizing the linear correlation existing between the current chroma block and its co-located reconstructed luma region. However, the predictor generated by CCLM can potentially have discontinuity with its adjacent regions since it does not consider the adjacent samples during its predictor generation. So, we propose a new method called CCLM Combined with Decoder-side Intra Mode Derivation (CCDIMD) to overcome this problem. The key idea of CCDIMD is a weighted combination of the predictor generated by CCLM and the predictor generated by an intra-prediction mode inferred using the intra mode derivation process of DIMD. Our proposed method shows BDBR gains respectively of -0.02%, -1.

show abstract

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

Zhu

Zhang

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

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, where only one single trained model is required. Different from the existing deep learning based classification problems, the hand-crafted features are also fed into 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.

show abstract

Decoder-Side Intra Mode Derivation with Texture Analysis in VVC Test Model

Cited by 3 publications

References 8 publications

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

CCLM combined with decoder-side intra mode derivation

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

Contact Info

Product

Resources

About