Chenggang Yan

A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors

High Efficiency Video Coding (HEVC) uses a very flexible tree structure to organize coding units, which leads to a superior coding efficiency compared with previous video coding standards. However, such a flexible coding unit tree structure also places a great challenge for encoders. In order to fully exploit the coding efficiency brought by this structure, huge amount of computational complexity is needed for an encoder to decide the optimal coding unit tree for each image block. One way to achieve this is to use parallel computing enabled by many-core processors. In this paper, we analyze the challenge to use many-core processors to make coding unit tree decision. Through in-depth understanding of the dependency among different coding units, we propose a parallel framework to decide coding unit trees. Experimental results show that, on the Tile64 platform, our proposed method achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.

Efficient Parallel Framework for H.264/AVC Deblocking Filter on Many-Core Platform

The H.264/AVC deblocking filter is becoming the performance bottleneck of H.264/AVC parallelization on many-core platform. Efficient parallelization of the deblocking filter on a many-core platform is challenging, because the deblocking filter has complicated data dependencies, which provide insufficient parallelism for so many cores. Furthermore, parallelization may have significant synchronization and load imbalance overhead. At present, research on the parallelizing deblocking filter on a many-core platform is rare and focuses on data-level parallelization. In this paper, we propose a three-step framework considering task-level segmentation and data-level parallelization to efficiently parallelize the deblocking filter. First, we review the entire deblocking filter process in 4 × 4 block edge-level and divide it into two parts: 1) boundary strength computation (BSC) and 2) edge discrimination and filtering (EDF), which increases the parallelism. Then, we apply the Markov empirical transition probability matrix and Huffman tree (METPMHT) to the BSC, which alleviate the load imbalance problem. Finally, we use an independent pixel connected area parallelization (IPCAP) for the EDF, which increases the parallelism and reduces the synchronization. In experiments, we apply our parallel method to the deblocking filter of the H.264/AVC reference software JM15.1 on the Tile64 platform without any Tile64 platform-based optimizations. Compared to the well-known 2D-wavefront method, the proposed method achieves on average 14.85, 17.83, and 10.60 times speed-up for QCIF, CIF, and HD videos using 62 cores, respectively.

Efficient HEVC to H.264/AVC Transcoding with Fast Intra Mode Decision

High Efficiency Video Coding (HEVC) standard will soon reach its final draft. To provide the widely deployed H.264/AVC devices with HEVC video contents, transcoding pre-encoded HEVC video into H.264/AVC format is highly necessary. Computational complexity of H.264 hinders real-time transcoding. In this paper, we propose an efficient HEVC to H.264 intra frame transcoder to accelerate the time-consuming H.264 intra mode decision while ensure rate distortion (RD) performance. The proposed transcoder incorporates a support vector machine (SVM) based macroblock (MB) partition mode decision and a fast prediction mode decision. Compared with the reference transcoder which employs exhaustive search mode decision, our proposed transcoder can save 68.83% of transcoding time with negligible 2.32% bit-rate increase on average.

Parallel deblocking filter for H.264/AVC implemented on Tile64 platform

For the purpose of accelerating deblocking filter, which accounts for a significant percentage of H.264/AVC decoding time, some researchers use multi-core platforms to achieve the required performance. We study the problem under the context of many-core systems. Parallelization of deblocking filter on many-core platform is challenging not only because deblocking filter has complicated data dependencies which provides insufficient parallelism for so many cores but also because parallelization may have significant synchronization overhead. We present a new method to exploit the implicit parallelism and reduce the synchronization overhead. We apply our implementation to the deblocking filter of the H.264/AVC reference software JM15.1 on Tile64 platform. The proposed method achieves up to 817%, 604% and 532% speedup for CIF, SD and HD videos compared to the well-known wavefront method using 62 cores, respectively.

Parallel deblocking filter for H.264/AVC on the TILERA many-core systems

For the purpose of accelerating deblocking filter, which accounts for a significant percentage of H.264/AVC decoding time, some studies use wavefront method to achieve the required performance on multi-core platforms. We study the problem under the context of many-core systems and present a new method to exploit the implicit parallelism. We apply our implementation to the deblocking filter of the H.264/AVC reference software JM15.1 on a 64-core TILERA and achieve more than eleven times speedup for 1280*720(HD) videos. Meanwhile the proposed method achieves an overall decoding speedup of 140% for the HD videos. Compared to the wavefront method, we also have a significant speedup 200% for 720*576(SD) videos.

Efficient Parallel Framework for HEVC Deblocking Filter on Many-Core Platform

Summary form only given. Many-core platforms are good candidates for speeding up High Efficiency Video Coding (HEVC) in the case that HEVC can provide sufficient parallelism. As the most promising proposal for parallelizing HEVC deblocking filter (DF), the order-changed parallel method (OCPM) changes the order of filtering and incurs considerable loss in coding efficiency. Meanwhile, the parallelism of OCPM still has some room for improvement. In this paper, we propose an efficient parallel framework for HEVC DF, which exploits the implicit parallelism and keeps the filtering order of DF unchanged. Compared with the well-known OCPM, experiments conducted on a 64-core system show that our proposed method saves averagely 37.18% and 37.93% DF time with different quantization parameters (QPs). Meanwhile, our proposed method improves coding efficiency, which achieves an average BD-rate reduction of 0.09%, 0.11% and 0.12% for Y, U and V components, respectively.

Large scale image understanding with non-convex multi-task learning

Large scale image understanding is drawing more and more attention from the researchers and industry. Inspired by the game theory and machine learning algorithm, this paper proposes a semantic dictionary to solve the key problem of visual polysemia and concept polymorphism in the large scale image understanding. The semantic dictionary characterizes the probability distribution between visual appearances and semantic concepts, and the learning of semantic dictionary is formulated into a minimization problem of the payoffs, where the players adjudge their strategies (i.e. the probability distribution) at each iteration. Non-convex multi-task learning is introduced to solve the above optimization problem. Finally, the wide applications of semantic dictionary are validated in our experiments, including the large scale semantic image search and image annotation.

High Power Diode Pumped Vertical Externalcavity Surface-emitting Lasers (VECSELS)

We describe the theoretical analysis and calculations of the 980 nm high-power diode-pumped vertical external cavity surface emitting laser (VECSEL). The VECSEL with active region of InGaAs/GaAsP/AlGaAs system can be operated near 500 mW in a single transverse mode.