Fast variable block-size motion estimation is a key issue for real-time applications of the H.264, whereas the subpixel refinement takes up much computational time as compared to integer-pixel motion estimation. We propose a new fast subpixel precision variable block-size motion-estimation scheme. This algorithm uses the statistical information, which comes from the motion activities of the macroblocks (MBs) in the previous frame, to predict the characteristics of MBs in the current frame. Additionally, the distortion values and motion vectors of MBs in the previous frame are also considered as prior knowledge, based on which we can make decisions on early mode selection and early termination, and on whether or not to skip some candidate modes and candidate checking points. The intermediate results of subpixel motion estimation are used together with the prior knowledge to reduce subpixel search time when searching for stationary blocks. Our new directional information strategy is used in both integer-pixel motion estimation and subpixel motion estimation to accelerate the search procedure. Moreover, our algorithm can eliminate the subpixel motion estimation of all the unselected subpartition modes. The computational resources can then be spent on the modes and locations that deserve to be searched more than others. Extensive experimental is been done, the results of which show that the speed of our approach is nearly five times that of the fast algorithms in H.264 JM, with a better peak signal-to-noise ratio and better bit performance.
In this paper, a real-time demosaicing generic model is presented, which is concise, easy to implement, resources-saving
and robust. With the model, multiple displaying demosaicing subsystems can be realized in terms of a Bayer
pattern color filter array. A LCD displaying subsystem is designed by FPGA, and the waveform simulated is presented.
The monitoring results of a real scene are captured and illustrated. To prove the LCD monitoring results, the application
background of the LCD subsystem is given, which is a multiple channel CMOS image sampling system. The three
images exposed synchronously in the background system and the pictures stored are shown, one of which is identical to
the scene monitored by the LCD subsystem. Finally, to demonstrate the generality of the model, the test results of a CRT
demosaicing displaying subsystem designed with the model in the background system are given.
Genetic Algorithm (GA) is derived from the mechanics of genetic adaptation in biological systems, which can search the global space of certain application effectively. The proposed algorithm introduces three parameters, fit<SUB>max</SUB>, fit<SUB>min</SUB>, and fit<SUB>ave</SUB> to measure how close the individuals are, so as to improve the Adaptive Genetic Algorithm (AGA) proposed by M. Sriniras. At the same time, the elitist strategy is employed to protect the best individual of each generation, and Remainder Stochastic Sampling with Replacement (RSSR) is employed in the proposed Improved Adaptive Genetic Algorithm (IAGA) to improve the basic reproduction operator. The proposed IAGA is applied to image segmentation. The experimental results exhibit satisfactory segmentation and demonstrate the learning capabilities of it. By determining p<SUB>c</SUB> and p<SUB>m</SUB> of the whole generation adaptively, it strikes a balance between the two incompatible goals: sustain the global convergence capacity and converge rapidly to global optimum.
An adaptive approach to small object segmentation based on Genetic Algorithms is proposed. A new parameter scale of the subject area's percentage is introduced in this method, which can overcome the P-tile method's defect of requiring the exact percentage of an object area, and meanwhile makes effective use of the small object's character. Genetic Algorithm forms the skeleton of the new approach, which can dynamically locate the optical threshold in the search space. The proposed algorithm can be extended to segment those images with object of arbitrary size by simply changing the set of the new parameter. Experiment results indicate that the proposed algorithm performs better segmentation quality and takes less computational time than conventional Otsu method.