A Bit-Plane Decomposition Matrix-Based VLSI Integer Transform Architecture for HEVC

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

In order to realize copyright protection and piracy tracking for ever-increasing videos, video information hiding has gained more and more attention. For the latest video coding standard — High Efficiency Video Coding (HEVC), most of the information hiding algorithms are based on the same characteristics as those in MPEG4 and H.264, such as DCT coefficients, intra prediction modules and motion vectors. However, few algorithms are reported to hide information based on the innovation modules introduced by HEVC. This paper has proposed a high capacity and multilevel information hiding algorithm based on PU partition modes in P-frames, which is considered to be one of the most important innovative features of HEVC. The proposed algorithm consists of two stages: first round calculation and modification process. The first round calculation is to record the PU partition modes selected by HEVC. The modification process is to force each of PU mode into one of the encoded groups according to the embedding binary bits, and then use the modified PU partition modes for the left HEVC encoding process. The experimental results show that the proposed algorithm can achieve high capacity and visual quality, and low bit rate increase, which significantly outperforms state-of-the-art works in embedding capacity.

High quality videos became an essential requirement in recent applications. High efficiency video coding (HEVC) standard provides an efficient solution for high quality videos at lower bit rates. On the other hand, HEVC comes with much higher computational cost. In particular, motion estimation (ME) in HEVC, consumes the largest amount of computations. Therefore, fast ME algorithms and hardware accelerators are proposed in order to speed‐up integer ME in HEVC. This study presents a fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC. In addition, centre adaptive search algorithm, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance. Experimental results show notable speed‐up in terms of encoding time and bit rate saving with tolerable peak signal‐to‐noise ratio (PSNR) quality degradation. The proposed fast search algorithms reduce the computational complexity of the HEVC encoder by 57%. This improvement is accompanied with a modest average PSNR loss of 0.014 dB and an increase by 0.6385% in terms of bit rate when compared with related works.

The tremendous growth of imaging applications for mechanical engineering increases the amount of image and video data used to capture the microstructural characteristics or mechanical behavior of materials as a sequence of frames. In this paper, we evaluate the performance of the new state-of-the-art High Efficiency Video Coding (HEVC) standard to compress efficiently video sequences of materials under tensile testing captured with ultra-high speed camera. Different lossless and lossy coding modes are considered and the compression ratios as well as reconstructed video quality are studied. Preliminary simulation results show that HEVC offers high coding efficiency and excellent reconstructed video quality expressed in terms of PSNR as well as SSIM while preserving data accuracy for the prediction of material tensile behavior at high loading speed. We also propose a discussion on the consequences of HEVC compression on image content analysis. We show that material strain's measurements are not affected by HEVC compression in a significant way.

Real time object tracking is the process of locating moving objects over time using the camera in video sequences in real time. The objective of object tracking is to associate target objects in consecutive video frames. Object tracking requires location and shape or features of objects in the video frames. Every tracking algorithm needs to detect moving object. So object detection is the preceding step of object tracking in computer vision applications. After that, detected object can be extracted by the feature of moving object to track that moving object into video scene. It is challenging task in image processing to track the objects into consecutive frames. Various challenges can arise due to complex object motion, irregular shape of object, occlusion of object to object and object to scene and real time processing requirements. Object tracking has a variety of uses, some of which are: surveillance and security, traffic monitoring, video communication, robot vision and animation.

Detection of fake high definition for HEVC videos based on prediction mode feature

In recent years, there has been a tremendous improvement in video coding algorithms. This improvement resulted in 2013 in the standardization of the first version of High Efficiency Video Coding (HEVC) which now forms the state-of-theart with superior coding efficiency. Nevertheless, the development of video coding algorithms did not stop as HEVC still has its limitations. Especially for complex textures HEVC reveals one of its limitations. As these textures are hard to predict, very high bit rates are required to achieve a high quality. Texture synthesis was proposed as solution for this limitation in previous works. However, previous texture synthesis frameworks only prevailed if the decomposition into synthesizable and non-synthesizable regions was either known or very easy. In this paper, we address this scenario with a texture synthesis framework based on detail-aware image decomposition techniques. Our techniques are based on a multiple-steps coarse-to-fine approach in which an initial decomposition is refined with awareness for small details. The efficiency of our approach is evaluated objectively and subjectively: BD-rate gains of up to 28.81% over HEVC and up to 12.75% over the closest related work were achieved. Our subjective tests indicate an improved visual quality in addition to the bit rate savings.

Face motion is the sum of rigid motion related with face pose and non-rigid motion related with facial expression. Both motions are coupled in the captured image so that they can not be easily recovered from the image directly. In this paper, a novel technique is proposed to recover 3D face pose and facial expression simultaneously from a monocular video sequence in real time. First, twenty-eight salient facial features are detected and tracked robustly under various face orientations and facial expressions. Second, after modelling the coupling between face pose and facial expression in the 2D image as a nonlinear function, a normalized SVD (N-SVD) decomposition technique is proposed to recover the pose and expression parameters analytically. A nonlinear technique is subsequently utilized to refine the solution obtained from the N-SVD technique by imposing the orthonormality constraint on the pose parameters. Compared to the original SVD technique proposed in [1], which is very sensitive to the image noise and numerically unstable in practice, the proposed method can recover the face pose and facial expression robustly and accurately. Finally, the performance of the proposed technique is evaluated in the experiments using both synthetic and real image sequences.

This paper presents a method for tracking a person in a video sequence in real time. In this method, the profile of color distribution characterises target's feature. It is invariant for rotation and scale changes. It is also robust to non-rigidity and partial occlusion of the target. We employ the mean-shift algorithm to track the target and to reduce the computational cost. Moreover, we incorporate the particle-filter into it to cope with a temporal occlusion of the target, and largely reduce the computational cost of the original particle-filter. Experiments show the availability of this method.

When ensuring aviation safety, the outboard environment awareness of the crew in low visibility conditions is especially important. The information about the runway condition and availability of any obstacles is crucial. There are ground-based obstacle detection systems, but currently only large airports are equipped with them. There are Enhanced Vision Systems designed for application on aircraft in low visibility conditions. The main goal of this research is to develop the means of runway obstacle recognition in low visibility conditions, which are to improve the capabilities of Enhanced Vision Systems. The research covers only the methods for static image object detection. The analysis of the runway markings, objects and possible obstacles is performed. Targets for acquisition are defined. The simulation of runway images is performed on full-flight simulator in low visibility conditions. The requirements for features descriptors, recognition and detection methods are defined and methods for research are defined. The paper provides evaluation of method applicability to runway pictures taken in poor visibility conditions above and below the decision height taking into account various characteristics. The covered methods solve the problem of detecting objects of the runway in low visibility conditions for static image. Conclusions about the possibility to use the studied methods in Enhanced Vision Systems are made. Further development of optimization methods is required to perform detection in video sequences in real time. The results of this work are relevant to the tasks of avionics, computer vision and image processing.

This paper addresses the enhancement of the spatial resolution of a video sequence from a low resolution video sequence in real time. The technique used, known as super-resolution reconstruction, exploits the relative motion from frame to frame that produces sub-pixel shifts. The algorithm, based on a previous version mapped onto a video encoder architecture, is oriented towards a hardware implementation and requires resources optimization. In order to achieve a good resolution improvement, the motion estimation algorithm must produce motion vectors as close to the real ones as possible. At the same time, this motion estimation must match real time requirements. Therefore, an exhaustive technique is applied in combination with a simple segmentation of each frame for a motion prediction refinement. Experimental results have been obtained for a set of video sequences subjected to different motion characteristics.

In this paper, we present a pixel-wise unified rate quantization (R-Q) model for a low-complexity rate control on configurable coding units of high efficiency video coding (HEVC). In the case of HEVC, which employs hierarchical coding block structure, multiple R-Q models can be employed for the various block sizes. However, we found that the ratios of distortions over bits for all the blocks are a nearly constant because of employment of the rate distortion optimization technique. Hence, one relationship model between rate and quantization can be derived from the characteristic of similar ratios of distortions over bits regardless of block sizes. Thus, we propose the pixel-wise unified R-Q model for HEVC rate control working on the multi-level for all block sizes. We employ a simple leaky bucket model for bit control. The rate control based on the proposed pixel-wise unified R-Q model is implemented on HEVC test model 6.1 (HM6.1). According to the evaluation for the proposed rate control, the average matching percentage to target bitrates is 99.47% and the average PSNR degradation is 0.76 dB. Based on the comparative study, we found that the proposed rate control shows low bit fluctuation and good RD performance, compared to R-lambda rate control for long sequences.

When a disaster occurs, video communication is an effective way to disseminate large quantities of important information. However, video coding standards such as High Efficiency Video Coding (HEVC) compress entire videos, whatever the contents are; at low bit rates, the quality of significant objects deteriorates. In this paper, an object-based video coding method is proposed to address this problem. The proposed method extracts objects on the basis of visual saliency and temporal correlation between frames. Subsequently, we execute pre-processing which degrades the background quality before encoding the video with HEVC. This method can reduce the bit rate while preserving target object quality. Experimental comparison with HEVC demonstrates the superior performance of the proposed method.

This paper presents a highly parallel motion estimation architecture for High Efficiency Video Coding (HEVC) encoder. The proposed architecture has 16 processing units operating in parallel to calculate the sum of absolute difference values of all possible variable prediction block sizes. Hence, it calculates the bit cost regarding every partition in order to find the best matching candidate in terms of bit cost. The proposed unit processes block sizes from 4×4 up to 64×64. The proposed architecture was prototyped, simulated and synthesized using 65nm TSMC CMOS technology. At 720 MHz clock frequency, the proposed architecture processes 2K (1920×1080) resolution at 30 fps with ±27 (55×55) pixel search range using full search algorithm. Moreover, the proposed architecture is a flexible one and it can be used with different search algorithms to process higher resolutions such as 4K (3840×2160) resolution with 30 fps rate. To the best of our knowledge, the proposed architecture is one of the first ASIC motion estimation architectures in the literature for HEVC.

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.