Implementation of Diamond Search Algorithm Using Parallel Processing Architecture

K. Shuma Roshini, M. Tejaswi
Gudlavalleru Engineering College, Gudlavalleru, A.P.India
International Journal of Engineering Research and Application (IJERA), Volume 3, Issue 6, 2013

   title={Implementation of Diamond Search Algorithm Using Parallel Processing Architecture},

   author={Roshini, K. Shuma and Tejaswi, M.},



Download Download (PDF)   View View   Source Source   



In video communication whole content of video cannot be stored without processing. So there is a need to compress the video before transmission and storage this process is called as video compression. Video compression plays an important role with regard to real-time scouting/video conferencing applications. Regarding the entire motion based video compression process, movement estimation is the most computationally expensive and time consuming process. Motion estimation is the key element in video compression. The Motion Estimation is a process which determines motion between two or more frames and finds best possible macro block. There are several algorithms on block matching to name a few, Full Search Motion estimation [FS], Three Step Search Motion Estimation [TSS], New Three Step Search Motion Estimation [NTSS], Four Step Search Motion Estimation [FSS], Diamond Search Motion Estimation [DS].Instead of trying to further reduce computational complexity of these algorithms it is better to implement these algorithms on parallel processing architecture. In this paper Diamond Search Algorithm is implementation on CPU and GPU.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1658 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

335 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: