Hybrid Approach for Video Compression Using Block Matching Motion Estimation

To discard the redundancy present in video some video compression technique are involved .Basically video is a collection sequential frames in a sequence. video compression means reducing the size of video . In video sequence there are two types of technique are present that are temporal redundancy and spatial redundancy. In this paper we discuss about hybrid technique .Hybrid means combination of any two or more than two technique like efficient three step search algorithm(E3SS) and cross hexagonal search algorithm (CHS) .In today’s date block matching algorithm for motion estimation is powerful technique for high compression ratio and to reduce computational complexity .The motion estimation calculate the position of pixel and It is a custom to calculate the pixel from current frame to reference frame .The main function of motion estimation is reducing the search point and redundancy present in video .The experiment result shows that the proposal algorithm performs better than previous proposed block matching algorithms and required less computation than other technique

A Novel Adaptive Search Range Algorithm for Motion Estimation Based on H.264

Motion estimation (ME) is very vital to video compression. Due to the adoption of the high precision of motion vector (MV) in H.264 encoder, the computational cost increases rapidly, and ME takes about 60% of the whole encoding time. In order to accommodate the new variable block size motion estimation strategy adopted in H.264, this paper proposes a novel adaptive search range(ASR) algorithm as a optimized part based on UMHexagonS. Not only we utilize the median_MVP and interframe information in our ASR algorithm but also a penalty function is included. Experimental results indicate that our proposed method reduces the computational complexity in a certain degree and enhances encoding efficiency but has few changes in the reconstructed image quality and bit rate

Clouds Motion Estimation from Ground-Based Sky Camera and Satellite Images

Estimation of cloud motion is a challenging task due to the non-linear phenomena of cloud formation and deformation. Satellite images processing is a popular tool used to study the characteristics of clouds which constitute major factors in forecasting the meteorological parameters. Due to the low resolution of satellite images, researchers have turned towards analyzing the high-resolution images captured by ground-based sky cameras. The first objective of this chapter is to demonstrate the different techniques used to estimate clouds motion and to compare them with respect to the accuracy and the computational time. The second aim is to propose a fast and efficient block matching technique based on combining the two types of images. The first idea of our approach is to analyze the low-resolution satellite images to detect the direction of motion. Then, the direction is used to orient the search process to estimate the optimal motion vectors from the high-resolution ground-based sky images. The second idea of our method is to use the entropy technique to find the optimal block sizes. The third idea is to imply an adaptive cost function to perform the matching process. The comparative study demonstrates the high performance of the proposed method with regards to the robustness, the accuracy and the computation time