180 research outputs found
On the impact of the GOP size in a temporal H.264/AVC-to-SVC transcoder in baseline and main profile
Scalable video coding is a recent extension of the advanced video coding H.264/AVC standard developed jointly by ISO/IEC and ITU-T, which allows adapting the bitstream easily by dropping parts of it named layers. This adaptation makes it possible for a single bitstream to meet the requirements for reliable delivery of video to diverse clients over heterogeneous networks using temporal, spatial or quality scalability, combined or separately. Since the scalable video coding design requires scalability to be provided at the encoder side, existing content cannot benefit from it. Efficient techniques for converting contents without scalability to a scalable format are desirable. In this paper, an approach for temporal scalability transcoding from H.264/AVC to scalable video coding in baseline and main profile is presented and the impact of the GOP size is analyzed. Independently of the GOP size chosen, time savings of around 63 % for baseline profile and 60 % for main profile are achieved while maintaining the coding efficiency
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%
Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions
PhDThe research presented in this thesis aims to extend the scalability range of the
wavelet-based video coding systems in order to achieve fully scalable coding with a
wide range of available decoding points. Since the temporal redundancy regularly
comprises the main portion of the global video sequence redundancy, the techniques
that can be generally termed motion decorrelation techniques have a central role in
the overall compression performance. For this reason the scalable motion modelling
and coding are of utmost importance, and specifically, in this thesis possible
solutions are identified and analysed.
The main contributions of the presented research are grouped into two
interrelated and complementary topics. Firstly a flexible motion model with rateoptimised
estimation technique is introduced. The proposed motion model is based
on tree structures and allows high adaptability needed for layered motion coding. The
flexible structure for motion compensation allows for optimisation at different stages
of the adaptive spatio-temporal decomposition, which is crucial for scalable coding
that targets decoding on different resolutions. By utilising an adaptive choice of
wavelet filterbank, the model enables high compression based on efficient mode
selection. Secondly, solutions for scalable motion modelling and coding are
developed. These solutions are based on precision limiting of motion vectors and
creation of a layered motion structure that describes hierarchically coded motion.
The solution based on precision limiting relies on layered bit-plane coding of motion
vector values. The second solution builds on recently established techniques that
impose scalability on a motion structure. The new approach is based on two major
improvements: the evaluation of distortion in temporal Subbands and motion search
in temporal subbands that finds the optimal motion vectors for layered motion
structure.
Exhaustive tests on the rate-distortion performance in demanding scalable video
coding scenarios show benefits of application of both developed flexible motion
model and various solutions for scalable motion coding
R-D Optimal Scalable Video Coding Using Soft Decision Quantization
In this thesis, we study the concept of scalable video coding as implemented in the extension to the H.264 video coding standard. Specifically, for the spatial and quality scalability scenarios, we propose an optimization algorithm based on the Soft Decision Quantization (SDQ) concept, which aims at jointly optimizing all layers being encoded. The performance of the algorithm was assessed in an SVC implementation. Experimental results show, that the proposed method significantly improves the coding efficiency when compared to an unmodified SVC encoder
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