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A Fast Sub-pixel Motion Estimation Algorithm for

H.264/AVC Video Coding

Weiyao Lin

, Krit Panusopone

, David M. Baylon

, Ming-Ting Sun

, Zhenzhong Chen

and Hongxiang Li

Institute of Image Communication and Information Processing, Dept. of Electronic Engineering,

Shanghai Jiao Tong University, Shanghai 200240, China

Advanced Technology Department, Mobile Devices & Home, Motorola, Inc.,

San Diego, CA 92121, USA

Dept. of Electrical Engineering, University of Washington, Seattle, WA 98195, USA

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

Dept. of Electrical and Computer Engineering, North Dakota State University, Fargo, ND USA

Abstract

Motion Estimation (ME) is one of the most time-consuming parts in video coding. The use of

multiple partition sizes in H.264/AVC makes it even more complicated when compared to ME in

conventional video coding standards. It is important to develop fast and effective sub-pixel ME

algorithms since (a) The computation overhead by sub-pixel ME has become relatively significant

while the complexity of integer-pixel search has been greatly reduced by fast algorithms, and (b)

Reducing sub-pixel search points can greatly save the computation for sub-pixel interpolation. In this

paper, a novel fast sub-pixel ME algorithm is proposed which performs a ‘rough’ sub-pixel search

before the partition selection, and performs a ‘precise’ sub-pixel search for the best partition. By

reducing the searching load for the large number of non-best partitions, the computation complexity

for sub-pixel search can be greatly decreased. Experimental results show that our method can reduce

the sub-pixel search points by more than 50% compared to existing fast sub-pixel ME methods with

negligible quality degradation.

I. Introduction

H.264/AVC is the state-of-the-art video coding standard established by ITU-T and ISO/IEC.

H.264/AVC uses many new techniques and is able to save more than 50% in bitrate while having

similar video quality compared to the MPEG-2 video coding standard [1].

Motion Estimation (ME) is one of the most time-consuming parts in video coding. Developing

fast algorithms for ME to reduce computational complexity in video coding has been an important and

challenging problem. In the H.264/AVC Joint Model (JM) [5], the ME process contains two stages:

integer pixel search over a large area and sub-pixel search around the best selected integer pixel. Since

H.264/AVC uses 7 partition sizes for inter-frame prediction (16×16, 16×8, 8×16, 8×8, 8×4, 4×8 and

4×4), the complexity of multi-partition ME is high [2]. It is becoming more critical to develop fast and

effective sub-pixel ME algorithms for H.264/AVC. Firstly, the computation overhead by sub-pixel

ME has become relatively significant while the complexity of integer-pixel search has been greatly

reduced by fast algorithms. For example, there have been integer-pixel ME algorithms [4

10, 16] that

only need between 3 and 5 integer search points to calculate the final integer Motion Vector (MV).

The computation in the 16-point sub-pixel search method used in the JM thus becomes comparatively

large. Secondly, typical sub-pixel searches require interpolating sub-pixel values for computing the

Sum of Absolute Difference (SAD). Reducing sub-pixel search points can also reduce the

interpolation computation time.

In this paper, a novel sub-pixel ME algorithm is proposed for H.264/AVC, which performs a

‘rough’ sub-pixel search before the partition selection, and performs a ‘precise’ sub-pixel search for

the best partition. By reducing the searching load for the large number of non-best partitions, the

computation complexity for sub-pixel search can be greatly decreased. Experimental results show that

the proposed algorithm can significantly reduce the number of sub-pixel search points compared to

other fast sub-pixel ME algorithms [6-9], with negligible quality degradation.

The rest of this paper is organized as follows. Section II reviews existing research on sub-pixel

ME. Section III provides in-depth analysis on how to further reduce the search points for sub-pixel

ME for multiple partitions. The proposed algorithm is described in Section IV. Section V shows the

experimental results and Section VI concludes the paper.

II. Related Work

Chen et al. [6] analyzed the difference between the integer-pixel matching error surface and the

sub-pixel matching error surface. According to Chen’s analysis, the integer-pixel matching error

surface is far from a unimodal surface inside the searching window due to the complexity of the video

content. The assumption of unimodal will easily result in trapping in a local minimum. However, for

the sub-pixel matching error surface, the unimodal surface assumption holds in most cases because of

the smaller search range of sub-pixel ME as well as the high correlation between sub-pixels due to the

sub-pixel interpolation.

There has been much research on fast sub-pixel ME [6-9, 17]. Most of these methods are based

on the unimodal surface assumption and perform the sub-pixel search in two steps:

1) Predict a sub-pixel MV (SPMV), and

2) Perform a small area search around the SPMV to obtain the final sub-pixel MV.

The method to get the sub-pixel predicted MV can be summarized in two ways: using

spatiotemporal information and modeling the Sum of Absolute Difference (SAD) surface.

Chen et al. [6] and Yang et al. [8] used spatiotemporal information to get the SPMVs. In [6], a

Center Biased Fractional Pixel Search (CBFPS) fast sub-pixel ME method is studied, where the MVs

of neighboring MBs were used to get the SPMV as in Eqn (1),

)%_( MVmvpredSPMV −=

(1)

where pred_mv is the MV prediction of the current partition (in sub-pixel resolution), MV is the best

integer-pixel MV of the current partition (β=4 in the 1/4-pixel case and β=8 in the 1/8-pixel case) and

% represents the modulo operation. In [8], a larger partition MV (e.g., 16x8 inter-mode MV takes a

16x16 MV as a reference) or previous frame MV was used to get the SPMV. If combined with the

SPMV from CBFPS, the accuracy of the SPMV can be greatly increased.

A more popular way to get the SPMV is to use a function (in most cases a second-order function)

to model the SAD surface [7, 9]. If the matching errors of the best integer-pixel MV and its

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