Adaptive Search Range Motion Estimation Using Neighboring MVDs

본 논문은 움직임벡터차의 확률을 이용하여 움직임 탐색 범위를 결정하는 새로운 고속 움직임 추정 방법을 제안한다. 제안된 방법은 일종의 적응적 탐색영역 방법이다. 움직임벡터차의 분포에 대한 조사하고 그 분포의 파라미터를 최대우도추정방법으로 추정한다. 이 추정된 분포를 이용하여 탐색 범위는 움직임벡터차의 지정된 확률에 의해 효과적으로 제한될 수 있음을 보인다. 실험적으로 제안된 방법의 성능이 화질면에서 전역탐색법과 유사하며 복잡도 면에서는 중대한 감소를 입증하였다. 뿐만 아니라, 제안된 방법이 기존의 적응적 탐색영역 방법에 비해 훨씬 더 효과적으로 탐색영역을 결정함을 보여주었다. This paper presents a new fast motion estimation method where search ranges are determined by the probabilities of motion vector differences (MVDs), which is an adaptive/dynamic search range (ASR) method. The MVDs' distribution is investigated and its parameter is estimated by the maximum likelihood estimator. With the estimated distribution, we show that the search ranges can be efficiently restricted by a prefixed probability for MVDs. Experimental results showed that the performance of the proposed method is very similar to that of the full search algorithm in PSNR but it enables significant reduction in the computational complexity. In addition, they revealed that the proposed method determine the search ranges much more efficiently than the conventional ASR methods.

Comparing with search pattern motion estimation (ME) algorithms, adaptive search range (ASR) algorithms are more fundamental, regular and flexible. In variable block size motion estimation (VBSME), ASR algorithms can be applied whether on a whole frame (frame level), or on an entire macroblock which includes up to forty-one blocks (macroblock level), or just on a single block (block level). In the other hand, in H.264/AVC, not the motion vectors (MVs) but the motion vector differences (MVDs) are coded and the median motion vector predictors (median-MVPs) are used to place the search centers. In this sense, it can be thought that the search windows (SWs) are centered at the positions pointed by median-MVPs, the search ranges (SRs) play the role of limiting MVDs. Thus it is reasonable for considering using MVDs to predict SRs. In this paper, one of the MB level and two of the block level, at all three MVD based SR prediction algorithms are proposed. VBSME based experiments are carried out to assess the proposed algorithms. Comparisons between the proposed three algorithms and the previously proposed one given in [8] are done in terms of encoding quality and computational complexity.

In this paper a new fast motion estimation method useful for video encoding in a noisy environment is presented. Motion estimation methods can broadly categorized as Block Matching (BM) methods, Pel Recursive (PR) methods, and Frequency Domain (FD) methods. Majority of the current fast motion estimation methods belong to block matching category. These methods typically achieve speedup by either reducing the number of search locations or by reducing the number of computations at each search location. Our proposed method is based on the pel recursive formulation. However, in order to achieve fast estimation, we operate on a block of pixels. To provide a robust behavior in a noisy environment we use a Total Least Squares (TLS) based estimation scheme which aims at estimating the true motion vectors. The simulation results indicate the speedup and robustness of the algorithm in noisy environment.

Motion estimation (ME) has widely been used in video codecs, such as MPEG and ITU-T H.261, to remove interframe redundancy. This paper describes a fast full-search ME method for programmable processors with a multiply-accumulator. It is based on the block matching method that evaluates block mismatch by the mean square error. In order to reduce the computational load of the ME, a fast two-dimensional FIR filtering algorithm is applied, and the redundancy of the overlapped area computation among the reference blocks is eliminated. When the block size is 16/spl times/16 and the number of reference blocks in the search window is 32/spl times/32, this method reduces the computational load to 22.6% without degrading the motion tracking capability.

This paper presents a novel feature set for steganalysis of motion vector-based steganography in H.264/AVC. First, the influence of steganographic embedding on the sum of absolute difference (SAD) and the motion vector difference (MVD) is analyzed, and then the statistical characteristics of these two aspects are combined to design features. In terms of SAD, the macroblock partition modes are used to measure the quantization distortion, and by using the optimality of SAD in neighborhood, the partition based neighborhood optimal probability features are extracted. In terms of MVD, it has been proved that MVD is better in feature construction than neighboring motion vector difference (NMVD) which has been widely used by traditional steganalyzers, and thus the inter and intra co-occurrence features are constructed based on the distribution of two components of neighboring MVDs and the distribution of two components of the same MVD. Finally, the combined features are enhanced by window optimal calibration, which utilizes the optimality of both SAD and MVD in a local window area. Experiments on various conditions demonstrate that the proposed scheme generally achieves a more accurate detection than current methods especially for videos encoded in variable block size and high quantization parameter values, and exhibits strong universality in applications.

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.

An enhanced constrained one-bit transform (C1BT)-based fast motion estimation (ME) method is proposed. Binary transform-based ME identifies the proper motion vector by transforming the 8-bit pixels in the original image into a low bit depth of the bit plane, substantially reducing the computational complexity and facilitating hardware implementation by using the number of non-matching points (NNMPs) instead of the sum of absolute difference (SAD). However, a motion block size is N × N , therefore the dynamic range of NNMP (0 ≤ NNMP ≤ N × N ) is decreased to 1/256 compared to that of the SAD with 8-bit images (0 ≤ SAD ≤ 256 × N × N ). The higher the NNMP between the current bit plane and the previous bit plane, the more probable that it will be the block most similar to each other. Therefore, the matching error criterion of NNMP is extended to improve ME performance. Experimental results show that the proposed algorithm improves the performance of ME accuracy by 0.27, 0.38 and 0.67 dB compared to the C1BT-based ME, two-bit transform (2BT)-based ME and 1BT-based ME, respectively.

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.

This paper presents an image-based velocity control method for a VTOL UAV. For increased autonomy, a realtime embedded global motion estimation process using efficient feature tracking was developed. The VTOL vehicle is assumed to be equipped with a minimum sensor suite — i.e. a camera, altitude sensor and IMU — and to hover over a featured flat plane. This paper first presents a fast feature-based global motion estimation method, and then proposes a control strategy based on this method. Finally, experimental results using a quadrotor demonstrate the performance of the proposed control strategy and feature-based motion estimation.

In this paper, an efficient and fast global motion estimation method is proposed. This method is a generic one that can accommodate various global motion models, ranging from a simple translation model to an eight parameters perspective model. At the first stage, a simple filter is implemented to classify different parts of the frames. Since pixels from homogeneous regions and highly textured regions do not contribute much in global motion estimation, it is proposed that they-be excluded from processing. The remained pixels are fed into a three-level hierarchical motion estimation system and a dynamic sprite is eventually generated. From the experimental results, we can conclude that the proposed global motion estimation system manages to speed up the processing time for sprite generation while maintaining quality in the reconstructed frames.

Multi-grid method is applied to optical flow estimation in ultrasound image sequences. The existing multigrid method is modified to accommodate the nonlinearity in the motion model. The nonlinear iteration, instead of the linear one, is proposed for the approximate solution in the finest grid. The proposed method was tested in both of panoramic imaging and the spatio-temporal noise reduction. The results showed that its performance was not distinguishable from the one-level method in spites of quite less computational burden.

Based on the statistical analysis of relation between CBP (Coded Block Pattern), MVD (Motion Vector Difference) and each coding mode, a fast mode selection algorithm based on macroblock-adaptive is proposed. In this algorithm, adaptive judgment strategies are adopted to filter partial coding mode quickly. The residual CBP, MVD information is used adequately to identify the relativity of intra and inter frame. It only needs to compute the radio distortion cost of mode with very high possibility according to judgment rule combining with RD cost. Experimental results show that at the expense of 3.8 percent increase of bit rate, the coding complexity is reduced and the efficiency of coding mode selection is increased greatly. The encoding speed and the PSNR (peak signal to noise ratio) increases 53.27% and 0.023 dB respectively in average.

The selective encryption is a commonly used encryption method of protecting video information about H.264/AVC. However, ensuring the effectiveness and minimizing the computation cost of the video encryption at the same time are difficult. In this paper, Motion Vector Difference (MVD) and Quantized coefficient (QC) are classified encrypted. It is found that MVD and QC can be selected to encrypt ensure the computational cost of encryption. The experimental results demonstrate that the proposed method of selecting different encryption methods according to the different parameters of quantization can realize that guaranteeing the effectiveness of video encryption with the purpose of saving the encryption cost.

Aiming to protect the video content and facilitate online video consumption, a perceptual encryption scheme is proposed for high efficiency video coding (HEVC) video. Based on RC4 algorithm, a key stream generation method is constructed, whose proportion of “1” and “0” can be regulated. During HEVC encoding, four kinds of syntax elements including motion vector difference (MVD)’ sign, MVD’s amplitude, sign of the luma residual coefficient and sign of the chroma residual coefficient, are encrypted by the regulated key stream. Experimental results and analysis show that the proposed scheme has good perceptual protection for the video content, and some advantages such as low computational cost, format-compliance and no bitrate increase can be achieved. It provides an effective resolution for the paid video-on-demand services in smart cities.

In view of the fact that the classical fast motion estimation methods are easy to fall into local optimum and suffer the high computational cost, the convergence of the motion estimation method based on the swarm intelligence algorithm is very slow. A new block motion estimation method based on single-dimension perturbation glowworm swarm optimization algorithm is proposed. Single-dimension perturbation is a local search strategy which can improve the ability of local optimization. The proposed method not only has overcome the defect of falling into local optimum easily by taking use of both the global search ability of glowworm swarm optimization algorithm and the local optimization ability of single-dimension perturbation but also has reduced the computation complexity by using motion vector predictor and terminating strategies in view of the characteristic of video images. The experimental results show that the performance of the proposed method is better than that of other motion estimation methods for most video sequences, specifically for those video sequences with violent motion, and the searching precision has been improved obviously. Although the computational complexity of the proposed method is slightly higher than that of the classical methods, it is still far lower than that of full search method.