1 / 17

Predictive 3D Search Algorithm for Multi-Frame Motion Estimation

Predictive 3D Search Algorithm for Multi-Frame Motion Estimation. Lim Hong Yin, Ashraf A. Kassim , Peter H.N de With IEEE Transaction on Consumer Electronics,2008. Outline. Overview of FME Algorithm Multi-Directional Hexagon Proposed 3D Search Scheme High-motion & Low-motion

aneko
Download Presentation

Predictive 3D Search Algorithm for Multi-Frame Motion Estimation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Predictive 3D Search Algorithm for Multi-Frame Motion Estimation Lim Hong Yin, Ashraf A. Kassim, Peter H.N de With IEEE Transaction on Consumer Electronics,2008

  2. Outline • Overview of FME Algorithm • Multi-Directional Hexagon • Proposed 3D Search Scheme • High-motion & Low-motion • Simulation Results & Conclusion

  3. Overview of FME Algorithm • In FME, the block-matching cost function increases monotonically as the search moves away from the position of the best match/global minimum • There is also a risk of the search getting trapped in a local minima  the same rate-distortion performance as the full search (FS) cannot be achieved

  4. Multi-Directional Hexagon • Next best position in relation to that of the current best position • Multi-Directional Hexagon pattern All points within a range of ± 2 from T1 are then evaluated and the direction of the best point from T1 (Next Best Position) is recorded.

  5. Multi-Directional Hexagon • Fixed Hexagon pattern Step1. Initial best point obtain using LDP(Large Diamond Pattern) 3 3 4 Step2. use fixed hexagon pattern 4 4 3 2 2 4 1 Step3. In the final refinement serach, the Small hexagon/diamond is used 1 2 1 1 1 1 1 2 2 1 1

  6. Multi-Directional Hexagon • Multi-Directional Hexagon pattern Step1. Initial best point obtain using LDP(Large Diamond Pattern) 3 3 Step2. use multi-directional hexagon pattern 4 2 4 3 4 2 Step3. In the final refinement serach, the Small hexagon/diamond is used 1 4 1 1 2 1 1 1 1 1 Results in lower number of search points !! 1

  7. 3D Search Pattern Scheme • In a 3D search, the search can proceed directly to points at nearby frames and there is no necessity to perform a new search from the window center for each reference frame  the computational time can be reduced!

  8. Proposed 3D Search Scheme • In a video sequence, a moving object is likely to keep a similar appearance within the adjacent frames. • The proposed 3D search pattern aims to perform the MV search by searching along the trajectory of object.

  9. Proposed 3D Search Scheme • 3D Multi-Directional Hexagon Search patterns(3D MD-HEXS) Flatted Hexagon Search Center Small Hexagon

  10. Proposed 3D Search Scheme • 3D Diamond Search patterns

  11. Proposed 3D Search Scheme • Step.1 3D-LDP is used for the initial search • Step.2 The position with the minimum cost function, Jminin the 3D-LDP is then used to select one of the four directional hexagon search patterns (repeated until Jmin lies in the 3D search center) • Step.3 3D-SDP is used to find best MV

  12. Multi-Hexagon-Grid Search For High-Motion • Most center-biased search algorithms such as DS and HEXBS perform poorly when used on high-motion sequences and when the search range is large • To overcome this problem, we propose the use of a large search pattern, specifically the Multi-Hexagon-Grid Search pattern for high-motion activity blocks.

  13. Multi-Hexagon-Grid Search For High-Motion • Use minimum cost functions (Jmin) to determine the motion activity  High-motion activity can be inferred when the Jmin obtained from the 3D search is significantly higher than the Jmin of adjacent blocks Base on TABLE III , we use a threshold for determine the motion activity : If the Jmin obtained from the 3D search is more than , we use the Multi-Hexagon-Grid Search pattern to perform a large motion search

  14. Simplifying Search for Low-Motion Blocks • The table shows the percentage of optimal MV obtained using FS, which is ±1 from the origin(%MV) • When the block is determined to be low-motion through the proposed criterion, the 3D search strategy is simplified to using only the 3D Small Diamond Search If the spatially adjacent MVs is within ±1 from the origin, then the current block is determined to be of low-motion activity. Low motion High motion

  15. Simulation Results • RD Optimization, CABAC encoding • Search Range :±16 & ±32 for QCIF & CIF • Referenced frame :5 high low

  16. Simulation Results

  17. Conclusion • The 3D MV predictors are used to obtain a more accurate search center while the 3D search patterns are designed to track the motion trajectories along the reference frames. This reduces the computational complexity while maintaining the search accuracy. • Implement a large motion search method utilizing the Multi-Hexagon Grid search pattern and saves on the search points for low-motion blocks by simplifying the 3D search • On average, the PSNR loss for our algorithm is less than 0.2 dB while a savings of more than 96% in computational time is achieved compared to FS.

More Related