Motion Estimation Presented By: Dr. S. K. Singh Department of Computer Engineering, Indian Institute of Technology (B.H.U.) Varanasi

Road Map Motion Estimation Spatial Redundancy. Temporal Redundancy Why Temporal Redundancy ? Video Coding Process Process of Determining Motion Vectors Techniques Used For Reducing Temporal Redundancy Classification of Motion estimation method Matching algorithm

Road Map Frequency domain matching algorithm Basic strategy for motion estimation Criteria of motion estimation Search Strategy Mean of Squared Error(MSE) Hierarchical Representation Of Digital Video Different Algorithms FIXED SEARCH AREA ALGORITHMS.

Road Map Scene adaptive search area algorithms Hierarchical and multi resolution fast block matching algorithms. Feature matching algorithm Predictive motion algorithm Multiple choice question mesh based me algorithm References

MOTION ESTIMATION Compression methodology in digital video processing there are some basic idea is to exploit(make use of) redundant data. We can find out two types of redundancy in Moving Picture 1.Spatial Redundancy. 2.Temporal Redundancy.

Why Temporal Redundancy ? Temporal redundancy occurs due to pixels in two video frames that have the same values in the same location. Exploiting temporal redundancy is one of the primary techniques in Video compression.

Video Coding Process Video Coding consists of two process 1.Processing for reducing Temporal Redundancy. 2.Processing for reducing Spatial Redundancy

Process of Determining Motion Vectors The process of determining the motion vectors is called motion estimation technique.

Techniques Used For Reducing Temporal Redundancy Motion Compensation process works as follows: 1.Partition of frames into macro blocks. Motion in frame will cause pixels within block to be in motion consistently in a consistent direction 2.Form of Vector Quantization, Codebook comprises of macro- blocks in reference frames, with the code-words of motion vectors used to predict values of macro-blocks to be compressed.

Motion Estimation Motion estimation is the process of determining motion vectors. It describes the transformation from one 2D image to another usually from adjacent frames in a video sequence. It is and ill-posed problem as the motion is in three dimensions(3D), However the images are a projection of the 3D scene onto a 2D plane.

Classification Of Motion Estimation Method Motion estimation method Mesh based estimationBlock based estimation method Time-domain Algorithms Frequency –domain analysis Matching algorithm Gradient ( differentiation) based algorithm 1.Optical flow field estimation. 2.Bayesian based estimation

Block Matching algorithms: 1.Exhaustive search block matching estimation 2.Fast algorithm 3.1-step search algorithm 4.3-step block search algorithm. Feature Matching algorithm: 1.PTSS 2.HPM, 3.SEA, 4.BFM, 5.BPM, 6.BBM Matching algorithm

Frequency Domain Matching Algorithm Phase correlation algorithm(DFT) Matching algorithm in (DCT) Domain Matching in Wavelet Domain

BASIC STRATEGY FOR MOTION ESTIMATION A large amount of the fast motion estimation scheme are based on matching algorithms All are composed of one or more of these basic strategies. The basic strategies of motion estimation : 1.Distance criterion: Distortion criterion for measuring distance between previous block and search area block.

Criteria of Motion Estimation There are following criteria : 1.MAE(Mean Absolute Error) 2.MSE(Mean Square Error Function) 3.SAD(Sum of Absolute Difference) 4.CCF(Cross-Correlation Function) 5.PDC(Pixel Difference Classification) 6.MAE(or MAD,SAD) are normally functioning due to their simplicity in hardware realization.

Search Strategy The stronghold(fastness)of the algorithm depends on the search strategy used. All fast motion estimation search algorithms use search area sub-sampling technique, where whole integer-pel are not used. Secondly, search area is again divided into two types: 1. Fixed Search Area 2.Adaptive Search Area.

Mean of Squared Error(MSE) Objective: To implement the block motion estimation, the candidate Video frame is partitioned into a set of non overlapping blocks. To determine the motion vector for each such of candidate block with respect to the given reference block.

Hierarchical Representation Of Digital Video Digital video No. of Frame No. of BlockNo. of Micro block No.of pixel values

Cont… For each above criteria,a square block of N×N pixel is considered. The intensity value of pixel at coordinate (n1,n2) in the frame-K is given by S(n1,n2,K). Where 0≤n1,n2≤N-1. Consider (K-l) as the past references frame

Cont… The motion vector(V) may narrate to the whole image known as global motion estimation. Motion estimation done unambiguous parts, such as rectangular blocks. Random shaped patches or even per pixel. The motion vectors may be represented by a translational model or many other models. it can approximate the motion of a real video camera, such as rotation and translation in all three dimensions and zoom.

Different Algorithms A.FIXED SEARCH AREA ALGORITHMS: 1.2DLOG, Three Step Search, 2.Orthogonal test search, New Three Step Search,4Step Search, 3.Cross Search, ODFS, PHODS, 4.OSA, SES, Cost reduction of 3Step Search

Cont… B.SCENE ADAPTIVE SEARCH AREA ALGORITHMS: DSRA,DSWA, BBGS, Global/Local in compensability analysis. Hierarchical And Multi resolution Fast Block Matching Algorithms: HPDS,HBMA, Pel Decimation Technique, Adaptive Pel Decimation Technique

Cont… C.FEATURE MATCHING ALGORITHM: PTSS, HPM, SEA,BFM,BPM,BBM D.PREDICTIVE MOTION ALGORITHM: 1.SBMA, 2. New Prediction Search Algorithm E.MESH BASED ME ALGORITHM: 1.HMMA 2.EBMA

Digital Video Processing Textbooks Yao Wang, Jörn Ostermann, Ya-Qin Zhang Prentice Hall, 2002 A.M. Tekalp, Prentice-Hall, 1995

References 1.Tekalp,A.Murat “Digital Video Processing.” P. cm (Prentice Hall signal processing series),ISBN , AL Bovik “Handbook of image and digital video processing", academic press, A Harcourt Science and Technology Company,ISBN , Yao Wang, Jörn Ostermann, and Ya-Qin Zhang Digital Video processing published in Prentice Hall, /24/2016Digital Video Processing26

References 4.R. G. Gonzalez and R. E. Woods. “Digital Image Processing”. Addison Wesley,2nd edition, 1992.