H.264/AVC에서 효율적인 정화소.부화소 움직임 추정

The performance of the motion-compensated prediction (MCP) in video coding is degraded by aliasing due to spatial sampling. To alleviate this problem in H.264/AVC, a low-pass filter is used by the fractional-pel motion estimation (FME) to suppress the aliasing component. However, the FME demands higher computational complexity on H.264/AVC encoding. In this work, we first perform a joint quantization and aliasing analysis on the H.264/AVC MCP process and show that the impact of the aliasing component can be alleviated by the quantization process. Then, we propose a fast motion estimation (ME) algorithm that uses the FME and the integer-pel motion estimation (IME) adaptively. The adaptive FME/IME algorithm examines the coding modes of the reference block for the current coding block, and then decides whether the FME or the IME should be applied to the current coding block. Experimental results show that the proposed adaptive FME/IME algorithm can help the encoder generate a bit stream at much lower computational complexity with small degradation in the coding gain as compared with a pure FME algorithm.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Motion estimation (ME) in high-definition H.264 video coding presents a significant design challenge for memory bandwidth, latency, and cost because of its large search range and various modes. To conquer this problem, this paper presents a low-latency and hardware-efficient ME design with three design techniques. The first technique on integer-pel ME (IME) adopts parallel instead of serial multiresolution search so that we can process 1080 p @ 60 fps videos with <formula formulatype="inline"><tex>$\pm$</tex></formula>128 search range within just 256 cycles, 5.95-KB buffers, and 213.7K gates. The second technique on fractional-pel ME (FME) uses a single-iteration six-point search to reduce the cycle count by half with similar gate count and negligible quality loss. The third technique applies a mode-filtering approach to further reduce the bandwidth and cycles and share the buffer of IME and FME. The final ME implementation with 0.13-<formula formulatype="inline"><tex>$\mu{\hbox {m}}$</tex></formula> process can support processing of 1080 p @ 60 fps with just 128.8 MHz, 282.6 K gates, and 8.54-KB buffer, which saves 60% gate count, and 68.9% SRAM buffers when compared with the previous design. </para>

With the development of fast motion estimation (ME) algorithms towards video compression standard H.264, burden on integer-pel ME is slightly reduced. However the integer-pel ME is becoming computationally complex due to demand of higher and higher resolution videos. On the other hand, there is always trade-off between the speed and performance of these search algorithms. This work improvises the performance of content awareness enabled integer-pel ME algorithm for encoding the fast and slow motion video sequences of high definition (HD) resolution category viz. 1280×720 and 1920×1080. The algorithm proposes a novel notion of 'integrity index' with focus on increasing the PSNR and reducing the bit rate. It is implemented in the frame work of x265 version 1.7 video encoder. Motion independent ME algorithm is analysed quantitatively in terms of parameters based on rate distortion and ME time. The proposed algorithm is found to be performing better on BD-Rate and BD-PSNR parameters for the videos of both resolutions under consideration. Observed increase in motion estimation time is less than four seconds compared to hexagonal search algorithm of ME which is the benchmark in fast ME algorithm.

Motion estimation (ME) is a time-consuming algorithm to find a matching block in the search area for video applications, such as video compression. Motion estimation algorithm includes full search algorithm and fast search algorithm. If the width of the motion estimation algorithm search area is twice the size of the block, half of the search area for adjacent blocks overlaps. In view of this, this paper proposes a search area data reuse method for fast search motion estimation. With the proposed data reuse implementation, only the first block needs to read the data of the entire search area from the off-chip memory. The other blocks only need to read half the original search area. In this way, time of access to external memory is reduced, and running time of the algorithm is also decreased. Experimental results of diamond search show that the search area data reuse methods can reduce the running time by 40% to 60% compared with the algorithm of no use data reuse, and it can also reduce the power consumption by 62% to 73%. Compared with other methods in the literature, the proposed method also performs better on running time and power consumption.

H.264 video coding standard supports several interprediction coding modes that use macroblock partitions with variable block sizes. Unfortunately, searching for optimal motion vectors of each possible partition incurs a heavy computational cost. In this paper, we propose a novel frame-level complexity control algorithm that determines for each macroblock which partitions are likely to be optimal. Motion vector search is carried out for only the selected partitions, thus reducing the complexity of the motion estimation step. The mode selection criteria is based on a measure of spatio-temporal activity within the macroblock. For each frame, the procedure guarantees that an execution time budget is met with minimum loss of coding efficiency. Simulation results show that the algorithm speeds up integerpel motion estimation by a factor of up to 40 with less than 0.2 dB loss in coding efficiency.

본 논문은 High Efficiency Video Coding (HEVC) GPU 기반 정수화소(integer-pel) 움직임 추정(Motion Estimation)을 고속화하기 위한 적응적인 탐색영역 결정 방법을 제안한다. 적응적인 탐색영역은 Motion Vector Difference (MVD)를 이용하여 결정한다. 먼저, 입력 영상의 MVD를 분석하여 입력 영상을 두 모델로 분류한다. 이후 분류된 각 모델의 MVD 특성에 따라 적응적인 탐색영역을 결정한다. 제안하는 알고리즘을 GPU 기반 정수화소 움직임 추정에 적용하기 위해 움직임 추정의 시작점은 이전 프레임의 Motion Vector (MV)로 결정한다. 위 과정은 CPU에서 이뤄지며, CPU는 움직임 추정의 시작점과 적응적인 탐색영역을 GPU에 전송한다. 이후 GPU는 정수화소 움직임 추정을 병렬로 수행한다. 제안하는 알고리즘은 참조 모델 대비 1.1%의 BD-rate 상승과 전체 부호화 시간의 37.9% 감소 및 951.2배 빠른 정수화소 움직임 추정 수행 시간을 얻는다. 또한, 적응적인 탐색영역이 적용되지 않은 단순 병렬화 알고리즘 대비 57.5%의 정수화소 움직임 추정 시간 감소와 0.6% BD-rate 상승을 얻는다. In this paper, we propose a new Adaptive Search Range (ASR) decision algorithm for accelerating GPU-based Integer-pel Motion Estimation (IME) of High Efficiency Video Coding (HEVC). For deciding the ASR, we classify a frame into two models using Motion Vector Differences (MVDs) then adaptively decide the search ranges of each model. In order to apply the proposed algorithm to the GPU-based ME process, starting points of the ME are decided using only temporal Motion Vectors (MVs). The CPU decides the ASR as well as the starting points and transfers them to the GPU. Then, the GPU performs the integer-pel ME. The proposed algorithm reduces the total encoding time by 37.9% with BD-rate increase of 1.1% and yields 951.2 times faster ME against the CPU-based anchor. In addition, the proposed algorithm achieves the time reduction of 57.5% in the ME running time with the negligible coding loss of 0.6%, compared with the simple GPU-based ME without ASR decision.

For video compression, motion estimation is popularly employed to exploit temporal correlation existing in video sequences. If we employ the full search block matching algorithm for estimating motion vectors, it requires very heavy computational complexity. Although several fast block matching algorithms have been proposed to solve this problem, they sacrifice their reconstructed image quality. In this paper, we derive optimal search patterns for fast block matching motion estimation. By analyzing the block matching algorithm as a function of the block size and the shape, we find optimal search patterns for initial motion estimation. The proposed idea can provide an analytical ground for the current MPEG-2 proposals. In addition, we propose a new fast motion estimation algorithm using adaptive search patterns, considering matching criteria and statistical properties of object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the frame difference of each block. By changing the search pattern adaptively, we can improve the motion prediction accuracy, while reducing the required computational complexity compared to other fast block matching algorithms.

Motion Estimation (ME) has been developed to remove redundant data contained in a sequence of image. And ME is an important part of video encoding systems, since it can significantly affect the output quality of an encoded sequences. However, ME requires a significant part of the encoding time, when using the Full Search (FS). For this reason, low complexity motion estimation algorithms are viable solutions. In this paper, we present an efficient algorithm based on spatio-temporal correlations and the direction of motion vectors that define the search pattern and the location of search center adaptively. Experiments show that the speedup improvement of the proposed algorithm over Diamond Search algorithm (DS) and Motion Vector Field Adaptive Search Technique (MVFAST) can be up to 0.5 ~ 3 times faster and the image quality improvement can be better up to 0.1 ~ 1(dB).

The H.264 provides various and useful features for improved coding efficiency and error robustness, but its encoder complexity is extremely high due to the computational time of ME (motion estimation) and mode decision of inter coding. In this paper, we propose a fast and simple class-based scheme to reduce the computational complexity of ME. The image in block base is classified to k classifications with SQCM (spatial-domain quickly classified method). Referring to the classified indices, we discriminate two methods of ME: one is pattern search for stationary macroblock, synchronous motion and unchanged object; other is fast inter prediction for instable or variant content of object. We can save the dispensable motion detection for different contents of video, named CQME (class-based quickly ME). Experimental results shows that the proposed method can achieve a reduction of 67% computational time on average, with an average PSNR loss of only 0.30 dB compared with the original H.264 reference software jm12.1.

This paper presents a novel block motion estimation scheme with edge alignment strategy on H.264/AVC video coding, whose computational complexity is linearly dependent on the number of allowed reference frames and block sizes using the full exhaustive search. Many fast block-matching algorithms reduce the computational complexity of motion estimation by carefully designing the search patterns with different shapes or sizes which have significant impact on the search speed and distortion performance. However, the search speed and the distortion performance conflict often with each other for these methods. In this paper, given a block in the current frame, we first apply a fast approximate method based on edge alignment to obtain a good initial motion vector as well as a tight initial bound of distortion measure. Then, considering the edge orientation of the block, a modified hexagon search is used to fine tune the motion vector in low computational complexity. The proposed algorithm also pays attentions to the characteristics of multiple reference frames and multiple block sizes in H.264/AVC. Computer simulation results show that the proposed method gives good performance and spans a new way to design a cost-effective real-time video coding system.

The full search block-matching algorithm is the simplest, but computationally very intensive approach. In recent years there was substantial progress in block motion estimation algorithms. Important milestones on this path were such algorithms as two-dimensional logarithmic search, three-step search, four step search, and diamond search. All these methods try to minimize the amount of search points applying sum of absolute differences (SAD) or equivalent metrics for each point. There were some works that studied partial SAD (PSAD) but mostly concentrated on a constant decimation factor. What we tried to study in this work is the performance of one of the best block matching search algorithms in combination with adaptive PSAD as a matching metric. The idea is that we use original motion estimation based on spatio-temporal correlation method, but instead of using SAD as a matching metric we use PSAD with adaptively chosen decimation factor. Our simulation results show that for high motion sequences PSNR degradation between full search and the proposed method was around 0.1-0.7 dB. The computational complexity reduction of 650-1700 times (compared with the full search) and 9 times (compared to the original method) is pretty big and may be well worth this video quality decrease. In the case of more static sequences PSNR degradation between full search and the original motion estimation method was around 0 dB. When we compare the original method with the proposed one the degradation increases to 0.1 dB. The computational complexity reduction was around 1600-1700 times (compared with the full search) and 5-7 times (compared to the original method).

As an essential component in applications such as video coding, autonomous navigation, and surveillance cameras, efficient and robust motion estimation is always required. This paper proposes a robust block-matching algorithm consisting of a rough matching step and a fine matching step for motion estimation. In the coarse matching step, an improved adaptive rood pattern search strategy combined with an anti-interference similarity criterion is developed to improve the computational efficiency and robustness. In the fine matching step, after performing a subpixel estimation procedure, a bilateral verification scheme is demonstrated to decrease the motion estimation errors caused by environmental disturbances. Experiments are carried out over popular video and image sequences, and several measurement indexes are used to quantify the performance of the proposed method and other motion estimation methods. Comparative analysis and quantitative evaluation demonstrate that the proposed method exhibits strong robustness and can achieve a good balance between computational efficiency and complexity.

The growing availability and widespread use of digital multimedia information and personal wireless communication devices have motivated development of several video compression standards over the past two decades. H.264/AVC (advanced video coding) is the newest international video coding standard approved by ITU-T as Recommendation H.264 and by ISO/IEC as International Standard 14496-10 (MPEG-4 part 10). Block motion estimation is one of the most important modules in the design of any video coder. It is widely used in many motion compensated video-coding standards like MPEG and ITU-T for multimedia and teleconference applications. Recently, a hexagon-shaped search pattern was introduced in fast block motion estimation, which has exhibited faster search speed. Based on an in-depth examination of the influence of search pattern on speed performance, we propose a efficient motion estimation algorithm using a hexagon-based search pattern to achieve further improvement. The proposed algorithm is to improve the execution speed of motion estimation algorithm by incorporating the direction and angle of previously processed motion vector. The proposed algorithm is investigated in comparison with hexagon search pattern, which demonstrates significant speedup gain over the hexagon-based search. Analysis shows that a speed improvement rate of the proposed algorithm over the hexagon search algorithm can be as high as over 80% for locating some motion vectors in certain scenarios. In short, the proposed algorithm can find a same motion vector with fewer search points than the existing hexagon-search algorithm. The proposed algorithm consistently reducing the number of search points in motion estimation module of video codec.

In this paper, we present a new and efficient block matching algorithm (BMA) for fast motion estimation. In block motion estimation algorithm, the search patterns and size of patterns has an effect on speed or distortion performance of BMAs. The proposed method uses (i) initial search center prediction, (ii) two different search patterns: square pattern and cross pattern with size 2 or 1 in subsequent search steps, and (iii) unrestricted search steps to find a motion vector. In this method, square pattern is uses as the initial pattern followed by cross and square patterns in subsequent steps. The combined effect of two subsequent patterns gives a partial octagon search pattern along the search direction. Experimental results shows that, proposed method is faster than currently used BMAs and maintains similar or better results.

The purpose of this work was to develop a physical phantom for testing the accuracy of cardiac wall motion estimation algorithms, and to use the phantom to evaluate several motion estimation and reconstruction methods, including a simultaneous image reconstruction/wall motion estimation algorithm we have developed. Our approach was to attach radioactive markers to the myocardial wall of a dynamic cardiac phantom and to trace the motion of the markers throughout the cardiac cycle via gated SPECT acquisition. Then, without moving the phantom, and after the markers were allowed to decay to negligible levels, the myocardium was injected with 99mTc and a gated SPECT scan was acquired. From the gated myocardial emission data, two wall motion estimation methods were evaluated. The first method was by applying an optical flow algorithm to an optimized OSEM reconstruction of the myocardial emission data. The second was by applying our simultaneous image reconstruction/motion estimation algorithm to the myocardial emission data. The error in the estimated motion fields was described by the average Euclidean distance between the motion of the markers and the estimated motion. Values of 0.15 and 0.14 were found for the average Euclidean distance for the optical flow method applied to OSEM and the simultaneous method, respectively. Image quality was also evaluated and, in agreement with our previous findings, the simultaneous method produced myocardial images with improved noise characteristics and better uniformity in terms of the activity distribution within the myocardium.