Decoder-Side Intra Mode Derivation For Next Generation Video Coding

Abdoli, Mohsen; Guionnet, Thomas; Raulet, Mickaël; Kulupana, Gosala; Blasi, Saverio

doi:10.1109/icme46284.2020.9102799

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…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]. For Li's scheme [21], it achieves 0.06%, 0.15%, and 0.12% bit rate reduction on average for luma and two chroma components, respectively.…”

Section: Coding Performance Comparisonmentioning

confidence: 99%

“…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. Li's scheme [21] re-constructs the MPM list with short and long range correlations.…”

Section: Coding Performance Comparisonmentioning

confidence: 99%

“…In analogous to motion estimation in inter coding, Intra Block Copy (IBC) [40] was introduced for screen content, which aims to exploit long distance correlations in an image. Two modes with high probability from gradient histogram were combined to generate a new intra mode in [1]. In [45], the local and nonlocal correlations were exploited for hybrid intra prediction, where the adaptive template matching prediction, combined local and nonlocal prediction, combined neighboring modes prediction were performed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Coding Performance Comparisonmentioning

confidence: 99%

Section: Coding Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

“…In order to generate an intra-predictor for a current block using DIMD in the luma channel, a HoG is calculated using the pixels in a given L-shape template area, which is a reconstructed region of 3-pixel thick adjacent to the current block, as shown in Figure 1(a) [4]. This template area is divided into three regions: 'above', 'left' and 'above-left'.…”

Section: Dimd For Luma Channelmentioning

confidence: 99%

DIMD histogram adjustment for screen content coding

Cheon,

Kim,

Jeon

2024

International Workshop on Advanced Imaging Technology (IWAIT) 2024

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“…Due to the network-based intra prediction that generates diverse textures, we devised a mapping method to convert the proposed network-based intra mode into an appropriate regular mode according to the prediction block characteristics within the LFNST procedure. A histogram of regular intra modes is derived based on the methodology of Decoder-Side Intra Mode Derivation (DIMD) to select a regular intra mode that fits the current prediction block [39]. Gradients g h and g v are calculated by applying the horizontal and vertical Sobel filters for each prediction sample obtained from the neural network-based mode to find a regular prediction mode suitable for the current prediction block.…”

Section: Mode Mapping For Lfnstmentioning

confidence: 99%

Coarse-to-Fine Network-Based Intra Prediction in Versatile Video Coding

Park,

Moon,

et al. 2023

Sensors

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After the development of the Versatile Video Coding (VVC) standard, research on neural network-based video coding technologies continues as a potential approach for future video coding standards. Particularly, neural network-based intra prediction is receiving attention as a solution to mitigate the limitations of traditional intra prediction performance in intricate images with limited spatial redundancy. This study presents an intra prediction method based on coarse-to-fine networks that employ both convolutional neural networks and fully connected layers to enhance VVC intra prediction performance. The coarse networks are designed to adjust the influence on prediction performance depending on the positions and conditions of reference samples. Moreover, the fine networks generate refined prediction samples by considering continuity with adjacent reference samples and facilitate prediction through upscaling at a block size unsupported by the coarse networks. The proposed networks are integrated into the VVC test model (VTM) as an additional intra prediction mode to evaluate the coding performance. The experimental results show that our coarse-to-fine network architecture provides an average gain of 1.31% Bjøntegaard delta-rate (BD-rate) saving for the luma component compared with VTM 11.0 and an average of 0.47% BD-rate saving compared with the previous related work.

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Decoder-Side Intra Mode Derivation For Next Generation Video Coding

Cited by 6 publications

References 10 publications

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

DIMD histogram adjustment for screen content coding

Coarse-to-Fine Network-Based Intra Prediction in Versatile Video Coding

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