Dynamically variable step search motion estimation algorithm and a dynamically reconfigurable hardware for its implementation

Motion Estimation (ME) is the most computationally intensive part of video compression and video enhancement systems. For the recently available High Definition (HD) video formats, the computational complexity of De full search (FS) ME algorithm is prohibitively high, whereas the PSNR obtained by fast search ME algorithms is low. Therefore, ill this paper, we present Dynamically Variable Step Search (DVSS) ME algorithm for Processing high definition video formats and a dynamically reconfigurable hardware efficiently implementing DVSS algorithm. The architecture for efficiently implementing DVSS algorithm. The simulation results showed that DVSS algorithm performs very close to FS algorithm by searching much fewer search locations than FS algorithm and it outperforms successful past search ME algorithms by searching more search locations than these algorithms. The proposed hardware is implemented in VHDL and is capable, of processing high definition video formats in real time. Therefore, it can be used in consumer electronics products for video compression, frame rate up-conversion and de-interlacing(1)

Motion estimation of planar curves and their alignment using visual servoing

Motion estimation and vision based control have been steadily improving research areas recently. Visual motion estimation is the determination of underlying motion parameters by using image data. Visual servoing on the other hand refers to the closed loop control of robotic systems using vision. Solving these problems with objects that have simple geometric features, such as points and lines is rather easy. However, these problems may imply certain challenges when we deal with curved objects that lack such simple features. This thesis proposes novel vision based estimation and control techniques that use object boundary information. Object boundaries are represented by planar algebraic curves. Decomposition of algebraic curves are used to extract features for motion estimation and visual servoing. Motion estimation algorithm uses the parameters of line factors resulting from the decomposition of the curve whereas visual servoing method employs the intersections of lines. Motion estimation algorithm is verified with several simulations and experiments. Visual servoing algorithm developed for the arbitrary alignment of a planar object is tested both with simulations on a 6 DOF Puma 560 robot and experiments on a 2 DOF SCARA robot. Results are quite promising

Motion estimation based frame rate conversion hardware designs

Frame Rate Up-Conversion (FRC) is the conversion of a lower frame rate video signal to a higher frame rate video signal. FRC algorithms using Motion Estimation (ME) obtain better quality results. Among the block matching ME algorithms, Full Search (FS) achieves the best performance since it searches all search locations in a given search range. However, its computational complexity, especially for the recently available High Definition (HD) video formats, is very high. Therefore, in this thesis, we proposed new ME algorithms for real-time processing of HD video and designed efficient hardware architectures for implementing these ME algorithms. These algorithms perform very close to FS by searching much fewer search locations than FS algorithm. We implemented the proposed hardware architectures in VHDL and mapped them to a Xilinx FPGA. ME for FRC requires finding the true motion among consecutive frames. In order to find the true motion, Vector Median Filter (VMF) is used to smooth the motion vector field obtained by block matching ME. However, VMFs are difficult to implement in real-time due to their high computational complexity. Therefore, in this thesis, we proposed several techniques to reduce the computational complexity of VMFs by using data reuse methodology and by exploiting the spatial correlations in the vector field. In addition, we designed an efficient VMF hardware including the computation reduction techniques exploiting the spatial correlations in the motion vector field. We implemented the proposed hardware architecture in Verilog and mapped it to a Xilinx FPGA. ME based FRC requires interpolation of frames using the motion vectors found by ME. Frame interpolation algorithms also have high computational complexity. Therefore, in this thesis, we proposed a low cost hardware architecture for real-time implementation of frame interpolation algorithms. The proposed hardware architecture is reconfigurable and it allows adaptive selection of frame interpolation algorithms for each Macroblock. We implemented the proposed hardware architecture in VHDL and mapped it to a low cost Xilinx FPGA

Fast search algorithms for digital video coding

PhD ThesisMotion Estimation algorithm is one of the important issues in video coding standards such as ISO MPEG-1/2 and ITU-T H.263. These international standards regularly use a conventional Full Search (FS) Algorithm to estimate the motion of pixels between pairs of image blocks. Since a FS method requires intensive computations and the distortion function needs to be evaluated many times for each target block. the process is very time consuming. To alleviate this acute problem, new search algorithms, Orthogonal Logarithmic Search (OLS) and Diagonal Logarithmic Search (DLS), have been designed and implemented. The performance of the algorithms are evaluated by using standard 176x 144 pixels quarter common intermediate format (QCIF) benchmark video sequences and the results are compared to the traditional well-known FS Algorithm and a widely used fast search algorithm called the Three Step Search (3SS), The fast search algorithms are known as sub-optimal algorithms as they test only some of the candidate blocks from the search area and choose a match from a subset of blocks. These algorithms can reduce the computational complexity as they do not examine all candidate blocks and hence are algorithmically faster. However, the quality is generally not as good as that of the FS algorithms but can be acceptable in terms of subjective quality. The important metrics, time and Peak Signal to Noise Ratio are used to evaluate the novel algorithms. The results show that the strength of the algorithms lie in their speed of operation as they are much faster than the FS and 3SS. The performance in speed is improved by 85.37% and 22% over the FS and 3SS respectively for the OLS. For the DLS, the speed advantages are 88.77% and 40% over the FS and 3SS. Furthermore, the accuracy of prediction of OLS and DLS are comparahle to the 3SS.Thepsatri Rajabhat University: Royal Thai Government