Extending HEVC with a Texture Synthesis Framework using Detail-aware Image Decomposition

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

Currently, most video resources on line are encoded in the H.264/AVC format. More fluent video transmission can be obtained if these resources are encoded in the newest international video coding standard: high efficiency video coding (HEVC). In order to improve the video transmission and storage on line, a transcoding method from H.264/AVC to HEVC is proposed. In this transcoding algorithm, the coding information of intraprediction, interprediction, and motion vector (MV) in H.264/AVC video stream are used to accelerate the coding in HEVC. It is found through experiments that the region of interprediction in HEVC overlaps that in H.264/AVC. Therefore, the intraprediction for the region in HEVC, which is interpredicted in H.264/AVC, can be skipped to reduce coding complexity. Several macroblocks in H.264/AVC are combined into one PU in HEVC when the MV difference between two of the macroblocks in H.264/AVC is lower than a threshold. This method selects only one coding unit depth and one prediction unit (PU) mode to reduce the coding complexity. An MV interpolation method of combined PU in HEVC is proposed according to the areas and distances between the center of one macroblock in H.264/AVC and that of the PU in HEVC. The predicted MV accelerates the motion estimation for HEVC coding. The simulation results show that our proposed algorithm achieves significant coding time reduction with a little loss in bitrates distortion rate, compared to the existing transcoding algorithms and normal HEVC coding.

In recent years,with the development of video coding technology and the increasing popularity of high definition(HD)video content,the ISO/IEC Moving Picture Experts Group(MPEG)and the ITU-T Video Coding Experts Group(VCEG)formed the joint collaborative team on video coding(JCV-VC)in 2010which aims to develop the next-generation video coding standard for HD video application,called High Efficiency Video Coding(HEVC).So far the test model HM has been used for performance evaluation and algorithm test.The final international standard of HEVC has been completed and published in the end of 2012based on the working plan of JCV-VC.In this paper,the key technologies about HEVC will be surveyed,specifically including the quad-tree structure of coding unit,prediction unit and transform unit,the advanced motion vector prediction and merging technology,angular intra prediction,DCT-based fractional pixel interpolation filter and context adaptive arithmetic coding etc.Finally the coding performance of some coding tools and complexity of HEVC are analyzed in detail.

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 in development and aims to revolutionize the compression world with a potential 50% bitrate saving over Advanced Video Coding (AVC, or H.264 / MPEG-4 AVC). Is history repeating itself? There was a 10 year gap between the standardization of MPEG-2 Video and AVC, with AVC eventually maturing to deliver a 50% bitrate saving over MPEG-2 Video. The ITU-T|ISO/IEC Joint Collaborative Team on Video Coding (JCT-VC) is aggressively aiming for a 2013 distribution date for the HEVC standard. Early investigations indicate there is scope for a 30–50% bitrate saving over AVC. Some may argue this is a technology without a home, but potential applications lie in IPTV over DSL where HEVC would increase service reach, or point-to-point links for newsgathering where bandwidth is at a premium. But the most applicable usages will be delivering the emerging 4Kx2K video format in the same channel capacity as HD AVC today and eventually more power-efficient coding for handheld devices. This paper explores the HEVC tool set enhancements that facilitate the compression gains over AVC, with example sequences compressed in the HM test model, and an exploration into the commercial opportunities which HEVC opens up in the television industry.

High Efficiency Video Coding (HEVC) is the latest video coding standard released as a successor of H.264/AVC, it expected to reduce the bitrate by 50% for the same perceptual quality. One of the major contributors to the higher compression performance of HEVC is the introduction of larger Coding Units (CU) with recursive partitioning mechanisms. This achievement in performance is accompanied by a high computational complexity, making this new standard very difficult to be embedded in current multimedia services and broadcast platforms. In this paper, a performance evaluation of All-Intra (AI) parallel realization of HEVC encoder is proposed, using a heterogeneous Octa-core CubieBoard4 platform that includes two quad-core ARM A7 and ARM A15. We used the OpenMP paradigm for parallel realization where each thread is assigned to a core processor to encode a separate frame. AI configuration is used to break coding dependencies between successive frames, which allow the parallel processing of a set of images. Experimental results shows that the proposed parallel realization of HEVC encoder, using eight threads, reduces the computational complexity to about 4.35×, without any loss in coding performance. These results do not match to the expected acceleration due to the heterogeneity of the platform.

High Efficiency Video Coding (HEVC), the latest video coding standard, aims to provide coding performance that is much superior to that of its predecessor, H.264, especially for high definition video. To fulfill this goal, the inter-prediction unit (PU) partitions of HEVC are more complex, and the search range of motion estimation (ME) is much larger. As a result, ME becomes a bottleneck in the design of the HEVC inter predictor. In response to this challenge, we developed a hardware-oriented integer ME algorithm and the related hardware implementation. Our proposed algorithm led to a decrease in terms of the Bjontegaard Delta rate when compared with the HEVC test model 15.0. The corresponding hardware solution benefitted from 2-D data reuse supported by horizontal and vertical reference SRAMs, on-chip memory reduction supported by $4\times4$ block compression, and a low-power sum of absolute difference (SAD) tree supported by PU-level chip selection. When adopting a $32\times32$ SAD tree, the minimum and maximum required working frequency for 4K $\times2\text{K}$ at 30 frames/s videos was [375, 500] MHz. These results demonstrated that our proposed solution offered desirable improvement in both coding speed and coding performance.

complexity of High Efficiency Video Coding (HEVC) poses a real challenge to HEVC encoder implementations. Particularly, the complexity stems from the HEVC quad-tree structure that also has an integral part in HEVC coding efficiency. This paper presents a Machine Learning (ML) based technique for pruning the HEVC quad-tree without deteriorating coding gain. We show how ML decision trees can be used to predict a depth interval for a quad-tree before the Rate-Distortion Optimization (RDO). This approach limits the number of RDO candidates and thus speeds up encoding. The proposed technique works particularly well with high-quality video coding and it is shown to accelerate the veryslow preset of practical Kvazaar HEVC intra encoder by 1.35× with 0.49% bit rate increase. Compared with the corresponding preset of ×265 encoder, Kvazaar is 2.12× as fast at a cost of under 1.21% bit rate overhead. These results indicate that the optimized Kvazaar is the leading open-source encoder in high-quality HEVC intra coding.

High efficiency video coding (HEVC), as an up-to-date video coding standard, is becoming widely used, due to its preeminent performance. We argue in this paper that the new HEVC encoder can be utilized to provide efficient features for video segmentation. Thus, we propose a novel method, which learns to segment videos in HEVC compressed domain. Specifically, three features are extracted from HEVC bitstreams. Then, these features are integrated for video segmentation in compressed domain, through the logistic regression which is learnt from training data. This way, the computational complexity on video decoding can be saved. Experimental results show that our method is significantly superior to other state-of-the-art methods in terms of F-measure.

Cross residual transform for lossless intra-coding for HEVC

Scalable High efficiency Video Coding (SHVC) is the scalable extension of the latest video coding standard High Efficiency Video Coding (HEVC). One of the key novelties introduced by SHVC is that it enables hybrid codec scalability. This basically means that the video layers can be encoded with different video standards providing backward compatibility between codecs. In this paper, we propose a software parallel SHVC decoder in hybrid codec scalability configuration. The proposed design consists of an Advanced Video Coding (AVC) decoder for the Base Layer (BL) and a HEVC decoder for the Enhanced Layer (EL). In order to perform Inter Layer Prediction (ILP), a communication of decoding states and outputs is established between the two decoders. While the native frame based parallelism is still allowed within the two decoders, the proposed design also enables the use of frame based parallelism between the two decoders. The proposed software design enables a real time decoding of the HEVC EL at 2160p60 while the AVC base layer is decoded at 1080p60 for ×2 spatial scalability.

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 emerging High Efficiency Video Coding (HEVC) standard adopts many advanced techniques with flexible combinations, which enables HEVC to achieve about 50% bit-rate reduction for similar perceptual video quality relative to the prior video coding standard H.264/Advanced Video Coding. However, the enormously increased encoding complexity of HEVC inevitably becomes one of the greatest challenges for real-time applications. Among all the factors resulting in the increase in encoding complexity of HEVC, the quad-tree structure for coding units (CUs) with different sizes and accordingly a large number of prediction modes is one critical reason. Thus, it is greatly desired to develop a fast mode decision method for HEVC to reduce the computational complexity. In this paper, considering that HEVC employs the quad-tree structure, and the distortion of each sub-CU can indicate whether the current mode is suitable for current CU, we explore the relationship between the impossible modes and the distribution of the distortions to help the encoder skip checking the unnecessary modes. Besides, since the residual values can reflect the prediction result directly, we propose a method to skip some motion estimation operations according to the distribution of the residuals. Experimental results show that the proposed method can save about 77% of encoding time with only about a 4.1% bit-rate increase compared with HM16.4 anchor, while compared with the fast mode decision method adopted in HM16.4, the proposed algorithm can save about 48% of encoding time with only about a 2.9% bit-rate increase.

High Efficiency Video Coding (HEVC) is a new video coding standard created by the JCT-VC group within ISO/IEC and ITU-T. HEVC is targeted to provide the same quality as H.264 at about half of the bit-rate and will replace soon to its predecessor in multimedia consumer applications. Up to now, only a few decoder implementations have been reported, most of them oriented to carry out a complexity analysis. In this paper, a DSP-based implementation of the HEVC HM9.0 decoder is presented. Up to the best of our knowledge, it is the first DSP-based implementation shown in the scientific literature. Several tests have been carried out to measure the decoder performance and the computational load distribution among its functional blocks. These results have been compared with the ones obtained with the decoder implementations reported up to date. Finally, based on the results obtained in previous works regarding software optimization of DSP-based decoders, realtime could be achieved for SD formats with a single DSP after optimizing our HEVC decoder. For HD formats, multi-DSP technology will be needed.

360 Video has become popular in recent years, as commercial interests in deploying Virtual Reality (VR) applications rise. This type of video is usually captured using multi-camera arrays, such as the GoPro Omni camera rig. After separate video streams are captured from multiple cameras, image stitching is applied to obtain a spherical representation of the scene, which spans 360 degrees horizontally and 180 degrees vertically, hence the name 360 video. In the existing workflow of 360 spherical video coding, the 360 video is projected onto the 2D plane with a projection format, such as equirectangular (ERP), cubemap (CMP), equal-area (EAP), octahedron (OHP), etc. Most, if not all, of the currently available 360 video content are provided in ERP format defined in longitude and latitude. Projection format conversion may be performed to convert the native ERP format to another format before coding is applied. Some projection formats contain more than one face, for example, CMP projects the sphere onto a cube of six faces or OHP projects the sphere onto an octahedron of eight faces. For these multi-face projection formats, the faces are packed onto a 2D rectangular picture with a frame packing method. For example, the six faces of CMP can be packed with 4x3 configuration, or 3x2 configuration. Finally, the frame packed picture is coded as a 2D conventional video. Existing video codecs are designed only considering conventional 2D video captured on a plane. When motion compensated prediction uses any samples outside of a reference picture's padding will be performed by simply copying the sample values from the picture boundaries. This repetitive padding method is referred as conventional 2D padding method, which is widely used in video coding standards such as H.264, High Efficiency Video Coding (HEVC). However, a 360 video encompasses video information on the whole sphere, and thus intrinsically has a cyclic property. When considering this cyclic property, the reference pictures of a 360 video no longer have boundaries, as the information they contain is all wrapped around a sphere. This cyclic property holds regardless of which projection format or which frame packing is used to represent the 360 video on a 2D plane. The paper presents a new geometry padding method for motion compensated prediction in 360 video coding. Unlike the conventional padding method for 2D video coding, the proposed geometry padding method extends samples outside of a 2D picture's boundaries using neighboring samples on the sphere. The geometry projection format is considered when performing padding. The corresponding sample outside of a face's boundary (which may come from another side in the same face or from another face), is derived with rectilinear projection. Each face is extended with geometry padding separately. When visualized, the extended faces using geometry padding show continuous texture representing natural extension of the texture inside the face. The proposed geometry padding method is implemented in the HEVC reference software HM-16.12 for the ERP and CMP projection formats. In the simulation, a total of sixteen 4K ERP video and eight 8K ERP video are used. For 8K ground truth 8K video, they are converted to 4K video in ERP and CMP projection formats, coded, and converted back to reconstructed 8K video in ERP format. For 4K ground truth video, they are directly coded in 4K ERP, or converted to CMP consisting of 75% of effective samples, coded, and converted back to reconstructed 4K video in ERP format. Then, the end-to-end spherical PSNR (S-PSNR) is calculated between the original 8K or 4K and the reconstructed 8K or 4K ERP video. BD-rate is calculated between the reference unmodified HEVC, which uses the conventional padding method, and HEVC modified with the proposed geometry padding method. Simulation results showed that geometry padding performs better. For 8K sequences, the proposed geometry padding gives on average luma (Y) BD-rate reduction of 0.3% for ERP and 0.8% for CMP, for 4K sequences, the proposed geometry padding gives on average Y BD-rate reduction of 0.2% for ERP and 1.0% for CMP. Comparing the gains in ERP format with the gains in CMP format, the improvement for CMP is larger. This is because CMP has six faces, therefore the improvement from geometry padding method affects more out-of-boundary samples. The proposed geometry padding method is also especially effective for sequences with fast motion. For example, it achieves BD rate reductions of 4.3%, 2.7%, 1.9%, and 2.5% for Glacier, Chairlift, Sb_in_lot, and Driving, respectively. These four sequences are all captured using moving cameras and have fast moving objects. As a result, the sequences contain a lot of across-the-face-boundary motion which can benefit from improved padding method. Detailed simulation results can be found in JVET contribution JVET-D0075 available at http://phenix.int-evry.fr/jvet/doc_end_user/documents/4_Chengdu/wg11/JVET-D0075-v3.zip.