The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
High dynamic range texture compression for graphics hardware
ACM SIGGRAPH 2006 Papers
FFT and Convolution Performance in Image Filtering on GPU
IV '06 Proceedings of the conference on Information Visualization
A Parallel and Pipelined Execution of H.264/AVC Intra Prediction
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Intra frame encoding using programmable graphics hardware
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Trellis-based R-D optimal quantization in H.263+
IEEE Transactions on Image Processing
Rate-constrained coder control and comparison of video coding standards
IEEE Transactions on Circuits and Systems for Video Technology
Accelerate video decoding with generic GPU
IEEE Transactions on Circuits and Systems for Video Technology
Fast Bit Rate Estimation for Mode Decision of H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A fast GPU-based motion estimation algorithm for H.264/AVC
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Fine-grained CUDA-based Parallel Intra Prediction for H.264/AVC
Proceedings of Network and Operating System Support on Digital Audio and Video Workshop
Motion vector extrapolation for parallel motion estimation on GPU
Multimedia Tools and Applications
Journal of Real-Time Image Processing
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Rate-distortion (RD)-based mode selections are important techniques in video coding. In these methods, an encoder may compute the RD costs for all the possible coding modes, and select the one which achieves the best trade-off between encoding rate and compression distortion. Previous papers have demonstrated that RD-based mode selections can lead to significant improvements in coding efficiency. RD-based mode selections, however, would incur considerable increases in encoding complexity, since these methods require computing the RD costs for numerous candidate coding modes. In this paper, we consider the scenario where software-based video encoding is performed on personal computers or game consoles, and investigate how multicore graphics processing units (GPUs) may be efficiently utilized to undertake the task of RD optimized intra-prediction mode selections in audio and video coding standards and H.264 video encoding. Achieving efficient GPU-based intra-mode decisions, however, could be nontrivial for two reasons. First, intra-mode decision tends to be sequential. Specifically, the mode decision of the current block would depend on the reconstructed data of the neighboring blocks. Therefore, the coding modes of neighboring blocks would need to be computed first before that of the current block can be determined. This dependency poses challenges to GPU-based computation, which relies heavily on parallel data processing to achieve superior speedups. Second, RD-based intramode decision may require conditional branchings to determine the encoding bit-rate, and these branching operations may incur substantial performance penalties when being executed on GPUs due to pipeline architectural designs. To address these issues, we analyze the data dependency in intra-mode decision, and propose novel greedy-based encoding orders to achieve highly parallel processing of data blocks. We also prove that the proposed greedy-based orders are optimal in our problem, i.e., they require the minimum number of iterations to process a video frame given the dependency constraints. In addition, we propose a method to estimate the coding rate suitable for GPU implementation. Experimental results suggest our proposed solution can be more than 50 times faster than the previously proposed parallel intraprediction, since our work can efficiently exploit the massive parallel opportunity in GPUs.