49,069 research outputs found
Lossless Intra Coding in HEVC with 3-tap Filters
This paper presents a pixel-by-pixel spatial prediction method for lossless
intra coding within High Efficiency Video Coding (HEVC). A well-known previous
pixel-by-pixel spatial prediction method uses only two neighboring pixels for
prediction, based on the angular projection idea borrowed from block-based
intra prediction in lossy coding. This paper explores a method which uses three
neighboring pixels for prediction according to a two-dimensional correlation
model, and the used neighbor pixels and prediction weights change depending on
intra mode. To find the best prediction weights for each intra mode, a
two-stage offline optimization algorithm is used and a number of implementation
aspects are discussed to simplify the proposed prediction method. The proposed
method is implemented in the HEVC reference software and experimental results
show that the explored 3-tap filtering method can achieve an average 11.34%
bitrate reduction over the default lossless intra coding in HEVC. The proposed
method also decreases average decoding time by 12.7% while it increases average
encoding time by 9.7%Comment: 10 pages, 7 figure
Transforms for intra prediction residuals based on prediction inaccuracy modeling
In intra video coding and image coding, the directional intra prediction is used to reduce spatial redundancy. Intra prediction residuals are encoded with transforms. In this paper, we develop transforms for directional intra prediction residuals. Specifically, we observe that the directional intra prediction is most effective in smooth regions and edges with a particular direction. In the ideal case, edges can be predicted fairly accurately with an accurate prediction direction. In practice, an accurate prediction direction is hard to obtain. Based on the inaccuracy of prediction direction that arises in the design of many practical video coding systems, we can estimate the residual variance and propose a class of transforms based on the estimated variance function. The proposed method is evaluated by the energy compaction property. Experimental results show that with the proposed method, the same amount of energy in directional intra prediction residuals can be preserved with a significantly smaller number of transform coefficients
Chroma intra-prediction with attention-based CNN architectures
Neural networks can be used in video coding to im- prove chroma intra-prediction. In particular, usage of fully- connected networks has enabled better cross-component pre- diction with respect to traditional linear models. Nonetheless, state-of-the-art architectures tend to disregard the location of individual reference samples in the prediction process. This paper proposes a new neural network architecture for cross-component intra-prediction. The network uses a novel attention module to model spatial relations between reference and predicted samples. The proposed approach is integrated into the Versatile Video Coding (VVC) prediction pipeline. Experimental results demonstrate compression gains over the latest VVC anchor compared with state-of-the-art chroma intra-prediction methods based on neural networks
Spatial Prediction in the H.264/AVC FRExt Coder and its Optimization
The chapter presents a review of the fast spatial prediction strategy that were designed for the Intra coding mode of the video coding standard H.264/AVC. At the end, the author presents an effective strategy based on belief propagation message passing
Chroma Intra Prediction with attention-based CNN architectures
Neural networks can be used in video coding to improve chroma
intra-prediction. In particular, usage of fully-connected networks has enabled
better cross-component prediction with respect to traditional linear models.
Nonetheless, state-of-the-art architectures tend to disregard the location of
individual reference samples in the prediction process. This paper proposes a
new neural network architecture for cross-component intra-prediction. The
network uses a novel attention module to model spatial relations between
reference and predicted samples. The proposed approach is integrated into the
Versatile Video Coding (VVC) prediction pipeline. Experimental results
demonstrate compression gains over the latest VVC anchor compared with
state-of-the-art chroma intra-prediction methods based on neural networks.Comment: 27th IEEE International Conference on Image Processing, 25-28 Oct
2020, Abu Dhabi, United Arab Emirate
Implementation Comparison of Intra Prediction Scheme
Due to increasing demand of video application such as video conference, video communication, video streaming and storage, video compression is required. Due to this requirement new international standard H.264 & H.265 HEVC are introduce for video compression. These standards improve video compression based on intra prediction. Intra prediction is a technique to enhance compression through use of neighboring pixels to predict current coding block. Intra prediction in H.264 / AVC is executed in the spatial domain, to predict with reference to neighboring samples of previously coded blocks to the left and / or above the block. There are total nine modes used to predict the current block. This work supports 4�4 block size and 16�16 block size for the prediction of the mode. The goal of this work is to reduce the computational complexity of intra prediction and to give accurate prediction result. In this work efficient intra prediction scheme is implemented and compared to literature
Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding
International audienceThe scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC), which introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels, including merge mode, inter2Nx2N, inter2NxN, interNx2N, interNxN, in-ter2NxnU, inter2NxnD, internLx2N and internRx2N, intra modes and inter-layer reference (ILR) mode. This can obtain the highest coding efficiency, but also result in very high coding complexity. Therefore, it is crucial to improve coding speed while maintaining coding efficiency. In this research, we have proposed a new depth level and inter mode prediction algorithm for quality SHVC. First, the depth level candidates are predicted based on inter-layer correlation, spatial correlation and its correlation degree. Second, for a given depth candidate, we divide mode prediction into square and non-square mode predictions respectively. Third, in the square mode prediction, ILR and merge modes are predicted according to depth correlation, and early terminated whether residual distribution follows a Gaussian distribution. Moreover, ILR mode, merge mode and inter2Nx2N are early terminated based on significant differences in Rate Distortion (RD) costs. Fourth, if the early termination condition cannot be satisfied, non-square modes are further predicted based on significant differences in expected values of residual coefficients. Finally, inter-layer and spatial correlations are combined with residual distribution to examine whether to early terminate depth selection. Experimental results have demonstrated that, on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%
- …