Chain Code-Based Occupancy Map Coding for Video-Based Point Cloud Compression

Self-learning residual model for fast intra CU size decision in 3D-HEVC

The latest video coding standard High Efficiency Video Coding (HEVC) is developed by Joint Collaborative Team on Video Coding(JCTVC). HEVC can achieve better compression performance than any other coding standards, which adopts the quadtree structured coding tree unit (CTU). In encoding process, the CTU is recursively split into four equally sized coding unit (CU)s. Along with the multiple sizes of the CUs, the number of prediction unit(PU) modes has been largely increased, which raises the computational complexity in HEVC encoder greatly. In this paper, a fast inter mode decision algorithm based on motion estimation (ME) and texture feature is proposed to speed up the PU mode decision in HEVC encoder. Experimental results demonstrate that the overall algorithm that is proposed can save 41% encoding time on average while the decrease of coding quality is negligible, when compared to that of the HM10.0 reference software.

High Efficiency Video Coding (HEVC) is a new generation video coding standard by Joint Collaborative Team on Video Coding (JCTVC). The development of HEVC aims at the compression for ultra high definition videos. In HEVC, the encoder adopts a quad-tree based video coding structure, in which each incoming frame is split into a set of coding tree blocks (CTB) for compression. Each CTB can be recursively split into coding unit (CU), predict unit (PU) and transform unit (TU). This paper proposes an early termination algorithm of recursive partition based on the Sobel edge density of CTB in HEVC intra coding mode. The proposed method can reduce the depth of recursive partition for each CTB and thus save a lot of encoding time. Experimental results by HM10.1 show that the average time savings is about 14.2% and the resultant increase of encoded BD-Rate is only 0.02%. The proposed method can achieve significant time savings with negligible BD-Rate increase.

Multi-view video is becoming a new trend in the multimedia industry. Due to large amount of data generated by such videos, efficient compression and fast encoding speed are hot research areas. Recently MV-HEVC, the multi-view extension of High Efficiency Video Coding (HEVC) standard has been introduced. It improves the coding efficiency but the encoding complexity has increased. In this paper we propose an early coding unit (CU) splitting decision of the dependent views from the coding tree units (CTUs) depth information of the base view. Maximum depth of the CU (dependent views) is limited to the maximum value of the depths of the eight neighboring CTUs of the co-located CTU in the base view. Experimental results reveal that our proposed technique achieves upto 63.08% reduction in encoding time, when compared with HTM reference software.

The High Efficiency Video Coding (HEVC) standard has shown large improvements in coding efficiency compared with previous standards. In particular, HEVC outperforms H.264/MPEG-4 Advanced Video Coding by up to 50% in terms of bitrate reduction for similar perceptual quality. This improvement is the result of the introduction of new coding tools, which enable the representation of data using fewer bits, but at the cost of long computation times. One of the most significant tools introduced by HEVC is the novel quadtree-based structure called the coding tree unit (CTU), which can be subsequently split into coding units (CUs), prediction units (PUs), and transform units (TUs), providing huge flexibility in the encoding. However, selecting the optimal tree partitioning requires the evaluation of a huge number of possibilities, which constitutes the most complex operation for the encoder. In order to tackle this task, this paper proposes a CTU/CU partitioning algorithm based on a pre-analysis stage. This stage performs a fast motion estimation that provides preliminary information to the encoder, including estimate distortion costs, which enables the building of the least-cost quadtree. On the basis of this tree, the evaluation performed by the encoder is restricted to a subset of PUs. After a thorough statistical analysis of numerous CU/PU combinations, we propose two different configurations that prioritize each of the target variables: coding efficiency and time reduction. In the former case, 58.09% of the encoding time can be saved at the cost of a 2.51% increase in Bjontegaard delta (BD)-rate, while a larger reduction of 63.08% is achieved with a 3.43% increase in BD-rate in the later case.

High Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this article, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.

In the latest video coding standard high efficiency video coding (HEVC), a quadtree-based coding unit (CU) partitioning scheme is adopted to better adapt to the characteristics of the video contents. However, the flexible scheme significantly increases the coding complexity, because large amount of possible CU partitioning modes should be traversed. In this paper, we propose a two-stage fast inter CU decision method to reduce the coding complexity of the HEVC encoders. In Stage I, all the CUs are classified into three categories based on the Bayesian method after the prediction unit (PU) mode $merge\,\,2N\times 2N$ is checked. Early CU pruning and early CU skipping are then applied to two of the categories, respectively. For the remaining category which is difficult to differentiate by the rate-distortion cost of the PU mode $merge\,\,2N\times 2N$ , an early CU pruning scheme based on conditional random fields is performed in Stage II, which takes both the local characteristics of the current CU and the coding information of its neighboring CUs into consideration. Experimental results show that our method can reduce 54.93% and 45.84% of the coding complexity on average with only 1.19% and 1.03% Bjontegaard delta bitrate increment under the random access main and the low delay P configurations, respectively.

High Efficiency Video Coding (HEVC) incorporates a flexible quad-tree block partitioning scheme and up to 35 prediction modes for intra coding. This enables a significant improvement in coding efficiency compared with previous standards. The superior coding efficiency of HEVC is achieved at the expense of greatly increased complexity. A fast Coding Unit (CU) depth decision algorithm is proposed to reduce the computational requirement for intra coding. An adaptive double thresholds scheme is employed to classify the homogeneity of video content. The classification is used to reduce the number of Rate Distortion (RD) evaluations in the CU depth decision process. The partition information of the temporally co-located CU and the spatially neighbouring CUs is jointly utilised to further narrow the depth range that needs to be evaluated. The computational complexity of HEVC intra coding is therefore reduced. Simulation results show that the proposed algorithm reduces encoding time by up to 57% compared with the HM 13.0 implementation, while having a negligible impact on rate distortion, with PSNR losses of 0.01dB and increases in bit-rate of 0.31%.

High Efficiency Video Coding (HEVC), a new video coding standard currently being established, adopts a quadtree-based Coding Unit (CU) block partitioning structure that is flexible in adapting various texture characteristics of images. However, this causes a dramatic increase in computational complexity compared to previous video coding standards due to the necessity of finding the best CU partitions. In this paper, a fast CU splitting and pruning method is presented for HEVC intra coding, which allows for significant reduction in computational complexity with small degradations in rate-distortion (RD) performance. The proposed fast splitting and pruning method is performed in two complementary steps: 1) early CU split decision and 2) early CU pruning decision. For CU blocks, the early CU splitting and pruning tests are performed at each CU depth level according to a Bayes decision rule method based on low-complexity RD costs and full RD costs, respectively. The statistical parameters for the early CU split and pruning tests are periodically updated on the fly for each CU depth level to cope with varying signal characteristics. Experimental results show that our proposed fast CU splitting and pruning method reduces the computational complexity of the current HM to about 50% in encoding time with only 0.6% increases in BD rate.

This paper presents a parallel motion estimation algorithm on Graphics Processing Units (GPU) with a GPU-based fast Coding Unit (CU) splitting mechanism for speeding up the execution speed of High Efficiency Video Coding (HEVC). Parallel motion estimation algorithms only offer motion vectors to HEVC encoder, but CU splitting decision in HEVC still needs more information to speed up the encoder. Therefore, a mechanism using GPU to signify encoder which CU depth can be split instantly using motion data is designed. With experiments on Kepler GK110 GPU, the proposed parallel algorithm gains over 510 times faster than the full search motion estimation in the HEVC reference software, and the proposed fast CU splitting mechanism can further save 23% total execution time when compared to the general encoder with parallel motion estimation.

High Efficiency Video Coding (HEVC) achieves significant coding performance over H.264. However, the performance gain is achieved at the cost of substantially higher encoding complexity, in which the coding tree unit (CTU) partition is one of the most time-consuming parts due to the rate-distortion optimization-based ergodic search of all possible quad-tree partitions. To address this problem, this paper proposes a texture-classification accelerated convolutional neural network (CNN)-based fast intra CU partition scheme to reduce the encoding complexity for intra-coding in HEVC, by taking into consideration of the heterogeneous texture characteristics into the CNN-based classification. First, a threshold-based texture classification model is developed to identify the heterogeneous and homogeneous CTUs, through jointly consideration of the CU depth, quantization parameter and texture complexity. Second, three different CNN structures are designed and trained to predict the CU partition mode for each CU layer in the heterogeneous CTUs. Finally, extensive experimental results show that the proposed scheme can reduce intra-mode encoding time by 62.13% with negligible BD-rate loss of 2.01%, consistently outperforming two state-of-the-art CNN-based schemes in terms of both coding performance and complexity reduction.

To satisfy the rapidly increasing demands for high resolution video, the Joint Collaborative Team on Video Coding (JCT-VC) developed a new video coding standard, i.e. High-efficiency Video Coding (HEVC). With the same perceptual video quality as H.264, HEVC can achieve a bit rate reduction of approximately 50%. However, the quad tree structured coding units (CUs) cause high computational complexity. In this paper, a novel algorithm is proposed to decrease the computational complexity of HEVC intra coding. We mainly use the total coding bits of CUs as threshold to terminate CUs' partition. Furthermore, we also consider the spatial correlation between the current LCU and its neighbor LCUs to predict the current largest CU minimum depth so that we can skip some certain CUs' rate distortion cost checking.

The high efficiency video coding (HEVC) is the new video coding standard, which obtains over 50% bit rate savings compared with H.264/AVC for the same perceptual quality. Intra-prediction coding in HEVC achieves high coding performance in expense of high computational complexity, due to the exhaustive evaluation of all available coding units (CU) sizes, with up to 35 prediction modes for each CU, selecting the one with the lower rate distortion cost, among other new features. This paper presents a Unified Architecture to form a novel fast HEVC intra-prediction coding algorithm, denoted as fast partitioning and mode decision. This approach combines a fast partitioning decision algorithm, based on decision trees, which are trained using machine learning techniques, and a fast mode decision algorithm, based on a novel texture orientation detection algorithm, which computes the mean directional variance along a set of co-lines with rational slopes using a sliding window over the prediction unit. Both algorithms proposed apply a similar approach, exploiting the strong correlation between several image features and the optimal CTU partitioning and the optimal prediction mode. The key point of the combined approach is that both algorithms compute the image features with low complexity, and the partition decision and the mode decision can also be taken with low complexity, using decision trees (if-else statements) and by selecting the minimum directional variance between a reduced set of directions. This approach can be implemented using any combination of nodes, obtaining a wide range of time savings, from 44 to 67%, and light penalties from 1.1 to 4.6%. Comparisons with similar state-of-the-art works show the proposed approach achieves the best trade-off between complexity reduction and rate distortion.

The joint collaborative team on video coding (JCT-VC) is developing the next-generation video coding standard which is called high efficiency video coding (HEVC). In the HEVC, there are three units in block structure: coding unit (CU), prediction unit (PU), and transform unit (TU). The CU is the basic unit of region splitting like macroblock (MB). Each CU performs recursive splitting into four blocks with equal size, starting from the tree block. In this paper, we propose a fast CU depth decision algorithm for HEVC technology to reduce its computational complexity. In2N×2N PU, the proposed method compares the rate-distortion (RD) cost and determines the depth using the compared information. Moreover, in order to speed up the encoding time, the efficient merge SKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental result shows that the proposed algorithm achieves the average time-saving factor of 44.84% in the random access (RA) at Main profile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate loss of 0.17% is also observed without significant loss of image quality.

The growing popularity of high resolution video and the continuously increasing demands for high quality video on mobile devices are producing stronger needs for more efficient video encoder. Concerning these desires, HEVC, a newest video coding standard, has been developed by a joint team formed by ISO/IEO MPEG and ITU/T VCEG. Its design goal is to achieve a 50% compression gain over its predecessor H.264 with an equal or even higher perceptual video quality. Motion Estimation (ME) being as one of the most critical module in video coding contributes almost 50%-70% of computational complexity in the video encoder. This high consumption of the computational resources puts a limit on the performance of encoders, especially for full HD or ultra HD videos, in terms of coding speed, bit-rate and video quality. Thus the major part of this work concentrates on the computational complexity reduction and improvement of timing performance of motion estimation algorithms for HEVC standard. First, a new strategy to calculate the SAD (Sum of Absolute Difference) for motion estimation is designed based on the statistics on property of pixel data of video sequences. This statistics demonstrates the size relationship between the sum of two sets of pixels has a determined connection with the distribution of the size relationship between individual pixels from the two sets. Taking the advantage of this observation, only a small proportion of pixels is necessary to be involved in the SAD calculation. Simulations show that the amount of computations required in the full search algorithm is reduced by about 58% on average and up to 70% in the best case. Secondly, from the scope of parallelization an enhanced TZ search for HEVC is proposed using novel schemes of multiple MVPs (motion vector predictor) and shared MVP. Specifically, resorting to multiple MVPs the initial search process is performed in parallel at multiple search centers, and the ME processing engine for PUs within one CU are parallelized based on the MVP sharing scheme on CU (coding unit) level. Moreover, the SAD module for ME engine is also parallelly implemented for PU size of 32×32. Experiments indicate it achieves an appreciable improvement on the throughput and coding efficiency of the HEVC video encoder. In addition, the other part of this thesis is contributed to the VLSI architecture design for finding the first W maximum/minimum values targeting towards high speed and low hardware cost. The architecture based on the novel bit-wise AND scheme has only half of the area of the best reference solution and its critical path delay is comparable with other implementations. While the FPCG (full parallel comparison grid) architecture, which utilizes the optimized comparator-based structure, achieves 3.6 times faster on average on the speed and even 5.2 times faster at best comparing with the reference architectures. Finally the architecture using the partial sorting strategy reaches a good balance on the timing performance and area, which has a slightly lower or comparable speed with FPCG architecture and a acceptable hardware cost