1 / 11

Optimal Motion Vector Search Algorithm

Optimal Motion Vector Search Algorithm. 6th Team 20032026 Kim, Hyun-Seok 20032072 Jang, Sun-Yean 20032077 Jung, Yu-Chul. ☆ Overview ☆. Terminology Block Matching Motion Vector Search Algorithms Considering Points Our Suggestion Implementation Outline References.

leanna
Download Presentation

Optimal Motion Vector Search Algorithm

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. Optimal Motion Vector Search Algorithm 6th Team 20032026 Kim, Hyun-Seok 20032072 Jang, Sun-Yean 20032077 Jung, Yu-Chul

  2. ☆ Overview ☆ • Terminology • Block Matching • Motion Vector Search Algorithms • Considering Points • Our Suggestion • Implementation Outline • References

  3. ☆ Terminology ☆ • Reference Frame : Frame in past (or future) used to predict • in current frame • Current Frame: Frame which is being analyzed to derive • motion vectors • Motion vector : The displacement of the closest matching block • in reference frame for a block in current frame • Motion Estimator : Process of determining the values of motion • vectors for each frame

  4. ☆ Block Matching ☆ • to find the “best” block from an earlier frame to construct • an area of the current frame • Goal is to find a vector where MAD(Mean Absolute Difference) • is minimum.

  5. ☆ Previous Approaches ☆ • Full Search • - Every possible block in the previous frame is examined • - Of all the blocks examined, the lowest MAE produced is chosen, and the motion vector from that block’s position to the current block’s position is generated. • - Problems : The most precise matching, but the most demanding in terms of computational complexity. (2w+1)^2 times

  6. ☆ Previous Approaches(cont’) ☆ • II. Conjugate Direction Search • - based on the assumption that the energy of the prediction error is • monotonically decreasing towards the optimum motion vector in the search • range. • 1) first, search along the horizontal row of blocks in the previous frame • The MAD is computed between each of these blocks • 2) Then, extent the search in the vertical direction, searching the column of blocks in the previous frame which have the same x-coordinate as the best matching block founded in step 1) • Comparing with the full search, • complexity is reduced noticeably  3+2w

  7. ☆ Previous Approaches (cont’) ☆ • III. Modified Logarithmic Search • - efficient and fast  2+7log(w) • - unable to search all of the locations at the boundaries of the search window, thus, it doesn’t always result in the optimum notion vector within the search window. However, its performance is very good for small displacements.

  8. ☆ Considering Points ☆ • Full search is simple and correct, but computational burden. • Other approaches are apt to get trapped in local minima, resulting in a significant loss in estimation accuracy, and compression performance in video coding, as compared to the Full search • ☆ What is needed? • Novel motion vector prediction technique • A highly localized search pattern • A computational constraint explicitly incorporated into the cost measure

  9. ☆ Our Suggestion ☆ Concepts 1. Employ a representative value based on bit information :To maximize the correctness in potential motion changes 2. Use memory hash table : To reduce computational time 3. Use Nearest Neighbor hood Algorithm :To reduce the possibility of getting trapped in local minima

  10. ☆ Implementation Outline ☆ • 최근 색채의 중요성이 강조-> • 소비자의 개성과 취향을 고려하고 트랜드를 쫓아 과감한 형태의 • 다양한 색채로 디자인된 제품들이 많이 출시됨 • 최근(작년) 출시된 스피커 비오랩(Beolab) ; • 소비자들의 주문을 통해 색상 선택하는 시스템 실시 • ->소비자들로 하여금 색상을 선택할 수 있는 기회를 주는 것이다. • “트랜드를 따르지 않는다. 우리는 스타일을 선도한다.” • -> B&O사의 BeoLab의 생산방식에 새롭게 채택한 방식은 • 다른 제품사들과는 더 적극적인 색채전략의 필요성을 고려한 방식 • 장엄하고 권위적인 사치가 아닌 안락함과 편안함을 제공해주는 보다 대중적인 오디오시스템의 이미지 마케팅을 위해 채택한 방식. 이것은 시장확대에 큰 역할 수행. Reference Frame Current Frame Alg 1 Computing time1 Similarity 1 Alg 2 Computing time2 Similarity 2 Alg 3 Computing time3 Similarity 3 Alg 4 Computing time4 Similarity 4

  11. ☆ References ☆ • Correlation Based Search Algorithms for Motion Estimation • Mohamed Alkanhal, Deepak Turgaga and Tsuhan Chen – E/CE of CMU, USA • (Picture Coding Symposium Portland, OR, April 21~23, 1999) • An Efficient Computation-Constrained Block-Based Motion Estimation Algorithm for Low Bit Rate Video Coding • Michael Gallant and Faouzi Kossentini – E/CE of UBC, Canada • Motion Vector Refinement for High-Performance Transcoding • Jeongnam Youn, Ming-Ting Sun, Fellow, IEEE, and Chia-Wen Lin • IEEE Transaction on Multimedia, Vol. 1, No. 1, March 1999 • Computation constrained fast-search motion estimation algorithm for TMN 7. In Q15-A-45, ITU-T Q15/SG16, Portland, Oregon, June 1997 • http://www.dcs.warwick.ac.uk/research/mcg/bmmc/index.html

More Related