Partition Map-Based Fast Block Partitioning for VVC Inter Coding
- URL: http://arxiv.org/abs/2504.18398v1
- Date: Fri, 25 Apr 2025 14:53:03 GMT
- Title: Partition Map-Based Fast Block Partitioning for VVC Inter Coding
- Authors: Xinmin Feng, Zhuoyuan Li, Li Li, Dong Liu, Feng Wu,
- Abstract summary: We propose a partition map-based algorithm to pursue fast block partitioning in inter coding.<n>Based on our previous work on partition map-based methods for intra coding, we analyze the characteristics of VVC inter coding.<n>We present a dual-threshold decision scheme to achieve a fine-grained trade-off between complexity reduction and rate-distortion (RD) performance loss.
- Score: 37.60581844783291
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Among the new techniques of Versatile Video Coding (VVC), the quadtree with nested multi-type tree (QT+MTT) block structure yields significant coding gains by providing more flexible block partitioning patterns. However, the recursive partition search in the VVC encoder increases the encoder complexity substantially. To address this issue, we propose a partition map-based algorithm to pursue fast block partitioning in inter coding. Based on our previous work on partition map-based methods for intra coding, we analyze the characteristics of VVC inter coding, and thus improve the partition map by incorporating an MTT mask for early termination. Next, we develop a neural network that uses both spatial and temporal features to predict the partition map. It consists of several special designs including stacked top-down and bottom-up processing, quantization parameter modulation layers, and partitioning-adaptive warping. Furthermore, we present a dual-threshold decision scheme to achieve a fine-grained trade-off between complexity reduction and rate-distortion (RD) performance loss. The experimental results demonstrate that the proposed method achieves an average 51.30% encoding time saving with a 2.12% Bjontegaard Delta Bit Rate (BDBR) under the random access configuration.
Related papers
- Object Segmentation-Assisted Inter Prediction for Versatile Video Coding [53.91821712591901]
We propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies.
With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions.
We show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences.
arXiv Detail & Related papers (2024-03-18T11:48:20Z) - CNN-based Prediction of Partition Path for VVC Fast Inter Partitioning
Using Motion Fields [28.294065058301932]
The Versatile Video Coding (VVC) standard has been recently finalized by the Joint Video Exploration Team (JVET)
VVC offers about 50% compression efficiency gain, at the cost of a 10-fold increase in encoding complexity.
We propose a method based on Convolutional Neural Network (CNN) to speed up the inter partitioning process in VVC.
arXiv Detail & Related papers (2023-10-20T22:26:49Z) - MMVC: Learned Multi-Mode Video Compression with Block-based Prediction
Mode Selection and Density-Adaptive Entropy Coding [21.147001610347832]
We propose a multi-mode video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.
For entropy coding, we consider both dense and sparse post-quantization residual blocks, and apply optional run-length coding to sparse residuals to improve the compression rate.
Compared with state-of-the-art video compression schemes and standard codecs, our method yields better or competitive results measured with PSNR and MS-SSIM.
arXiv Detail & Related papers (2023-04-05T07:37:48Z) - Efficient VVC Intra Prediction Based on Deep Feature Fusion and
Probability Estimation [57.66773945887832]
We propose to optimize Versatile Video Coding (VVC) complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation.
Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.
arXiv Detail & Related papers (2022-05-07T08:01:32Z) - Deep Learning-Based Intra Mode Derivation for Versatile Video Coding [65.96100964146062]
An intelligent intra mode derivation method is proposed in this paper, termed as Deep Learning based Intra Mode Derivation (DLIMD)
The architecture of DLIMD is developed to adapt to different quantization parameter settings and variable coding blocks including non-square ones.
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 Versatile Video Coding (VVC) test model.
arXiv Detail & Related papers (2022-04-08T13:23:59Z) - A Holistically-Guided Decoder for Deep Representation Learning with
Applications to Semantic Segmentation and Object Detection [74.88284082187462]
One common strategy is to adopt dilated convolutions in the backbone networks to extract high-resolution feature maps.
We propose one novel holistically-guided decoder which is introduced to obtain the high-resolution semantic-rich feature maps.
arXiv Detail & Related papers (2020-12-18T10:51:49Z) - EfficientFCN: Holistically-guided Decoding for Semantic Segmentation [49.27021844132522]
State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN)
We propose the EfficientFCN, whose backbone is a common ImageNet pre-trained network without any dilated convolution.
Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost.
arXiv Detail & Related papers (2020-08-24T14:48:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.