- Research Article
- Open Access

# A Content-Motion-Aware Motion Estimation for Quality-Stationary Video Coding

- Meng-Chun Lin
^{1}Email author and - Lan-Rong Dung
^{1}

**2010**:403634

https://doi.org/10.1155/2010/403634

© M.-C. Lin and L.-R. Dung. 2010

**Received:**31 March 2010**Accepted:**1 August 2010**Published:**18 August 2010

## Abstract

The block-matching motion estimation has been aggressively developed for years. Many papers have presented fast block-matching algorithms (FBMAs) for the reduction of computation complexity. Nevertheless, their results, in terms of video quality and bitrate, are rather content-varying. Very few FBMAs can result in stationary or quasistationary video quality for different motion types of video content. Instead of using multiple search algorithms, this paper proposes a quality-stationary motion estimation with a unified search mechanism. This paper presents a content-motion-aware motion estimation for quality-stationary video coding. Under the rate control mechanism, the proposed motion estimation, based on subsample approach, adaptively adjusts the subsample ratio with the motion-level of video sequence to keep the degradation of video quality low. The proposed approach is a companion for all kinds of FBMAs in H.264/AVC. As shown in experimental results, the proposed approach can produce stationary quality. Comparing with the full-search block-matching algorithm, the quality degradation is less than 0.36 dB while the average saving of power consumption is 69.6%. When applying the proposed approach for the fast motion estimation (FME) algorithm in H.264/AVC JM reference software, the proposed approach can save 62.2% of the power consumption while the quality degradation is less than 0.27 dB.

## Keywords

- Video Sequence
- Quality Degradation
- Speedup Ratio
- Motion Estimation Algorithm
- Aliasing Problem

## 1. Introduction

Motion Estimation (ME) has been proven to be effective to exploit the temporal redundancy of video sequences and, therefore, becomes a key component of multimedia standards, such as MPEG standards and H.26X [1–7]. The most popular algorithm for the VLSI implementation of motion estimation is the block-based full search algorithm [8–11]. The block-based full search algorithm has high degree of modularity and requires low control overhead. However, the full search algorithm notoriously needs high computation load and large memory size [12–14]. The highly computational cost has become a major problem on the implementation of motion estimation.

To reduce the computational complexity of the full-search block-matching (FSBM) algorithm, researchers have proposed various fast algorithms. They either reduce search steps [12, 15–22] or simplify calculations of error criterion [8, 23–25]. Some researchers combined both step-reduction and criterion-simplifying to significantly reduce computational load with little degradation. By combining step-reduction and criterion-simplifying, some researchers proposed two-phase algorithms to balance the performance between complexity and quality [26–28]. These fast algorithms have been shown that they can significantly reduce the computational load while the average quality degradation is little. However, a real video sequence may have different types of content, such as slow-motion, moderate-motion, and fast-motion, and little quality degradation in average does not imply the quality is acceptable all the time. The fast block-matching algorithms (FBMAs) mentioned above are all independent of the motion type of video content, and their quality degradation may considerably vary within a real video sequence.

Few papers present quality-stationary motion estimation algorithms for video sequences with mixed fast-motion, moderate-motion, and slow-motion content. Huang et al. [29] propose an adaptive, multiple-search-pattern FBMA, called the A-TDB algorithm, to solve the content-dependent problem. Motivated by the characteristics of three-step search (TSS), diamond search (DS), and block-based gradient descent search (BBGDS), the A-TDB algorithm dynamically switches search patterns according to the motion type of video content. Ng et al. [30] propose an adaptive search patterns switching (SPS) algorithm by using an efficient motion content classifier based on error descent rate (EDR) to reduce the complexity of the classification process of the A-TDB algorithm. Other multiple search algorithms have been proposed [31, 32]. They showed that using multiple search patterns in ME can outperform stand-alone ME techniques.

Instead of using multiple search algorithms, this paper intends to propose a quality-stationary motion estimation with a unified search mechanism. The quality-stationary motion estimation can appropriately adjust the computational load to deliver stationary video quality for a given bitrate. Herein, we used the subsample or pixel-decimation approach for the motion-vector (MV) search. The use of subsample approach is two-folded. First, the subsample approach can be applied for all kinds of FBMAs and provide high degree of flexibility for adaptively adjusting the computational load. Secondly, the subsample approach is feasible and scalable for either hardware or software implementation. The proposed approach is not limited for FSBM, but valid for all kinds of FBMAs. The proposed approach is a companion for all kinds of FBMAs in H.264/AVC.

Articles in [33–38] present the subsample approaches for motion estimation. The subsample approaches are used to reduce the computational cost of the block-matching criterion evaluation. Because the subsample approaches always desolate some pixels, the accuracy of the estimated MVs becomes the key issue to be solved. As per the fundamental of sampling, downsampling a signal may result in aliasing problem. The narrower the bandwidth of the signal, the lower the sampling frequency without aliasing problem will be. The published papers [33–38] mainly focus on the subsample pattern based on the intraframe high-frequency pixels (i.e., edges). Instead of considering spatial frequency bandwidth, to be aware of the content motion, we determine the subsample ratio by temporal bandwidth. Applying high subsample ratio for slow motion blocks would not reduce the accuracy for slow motion or result in large amount of prediction residual. Note that the amount of prediction residual is a good measure of the compressibility. Under a fixed bit-rate constraint, the compressibility affects the compression quality. Our algorithm can adaptively adjust the subsample ratio with the motion-level of video sequence. When the interframe variation becomes high, we consider the motion-level of interframe as the fast-motion and apply low subsample ratio for motion estimation. When the interframe variation becomes low, we apply high subsample ratio for motion estimation.

## 2. Generic Subsample Algorithm

*N*-by-

*N*,

*R(i,j)*is the luminance value at of the current macroblock (CMB). The is the luminance value at of the reference macroblock (RMB) which offsets from the CMB in the searching area -by- . is the subsample mask for the subsample ratio -to- as shown in (2) and the subsample mask is generated from basic mask (BM) as shown in (3), When the subsample ratios are fixed at powers of two because of regularly spatial distribution, these ratios are 16 : 16, 16 : 8, 16 : 4, and 16 : 2, respectively. These subsample masks can be generated in a 16-by-16 macroblock by using (3) and are shown in Figure 2. From (3), given a subsample mask generated, the computational cost of SSAD can be lower than that of SAD calculation, hence, the generic subsample algorithm can achieve the goal of power-saving with flexibly changing subsample ratio. However, the generic subsample algorithm suffers aliasing problem for high-frequency band. The aliasing problem will degrade the validity of motion vector (MV) and obviously result in a visual quality degradation for some video sequences. The next section will describe how the high-frequency aliasing problem occurs for subsample algorithm in detail,

## 3. High-Frequency Aliasing Problem

According to sampling theory [41], the decrease of sampling frequency will result in aliasing problem for high-frequency band. On the other hand, when the bandwidth of signal is narrow, higher downsample ratio or lower sampling frequency is allowed without aliasing problem. When applying the generic subsample algorithm for video compression, for high-variation sequences, the aliasing problem occurs and leads to considerable quality degradation because the high-frequency band is messed up. Papers [42, 43] hence propose adaptive subsample algorithms to solve the problem. They employed the variable subsample pattern for spatial high-frequency band, that is, edge pixels. However, the motion estimation is used for interframe prediction and temporal high-frequency band should be mainly treated carefully. Therefore, we determine the subsample ratio by the interframe variation. The interframe variation can be characterized by the motion-level of content. The ZMVC is a good sign for the motion-level detection because it is feasible for measurement and requires low computation load. The high ZMVC means that the interframe variation is low and vice versa. Hence, we can set high subsample ratio for high ZMVCs and low subsample ratio for low ZMVCs. Doing so, the aliasing problem can be alleviated and the quality can be frozen within an acceptable range.

*i*th GOP ( ) is defined as (5), where is the average PSNRY of

*i*th GOP using the full-search block-matching (FSBM) and is the average PSNRY of

*i*th GOP with specific subsample ratio (SSR). From Figure 3, although the video sequence "table" is, in the literature, regarded as a moderate motion, there exists the high interframe variation between the third GOP and the seventh GOP. Obviously, applying the higher subsample ratios may result in serious aliasing problem and higher degree of quality degradation. In contrast, between the eleventh GOP and the twentieth GOP, the quality degradation is low for lower subsample ratios. Therefore, we can vary the subsample ratio with the motion-level of content to produce quality-stationary video while saving the power consumption when necessary. Accordingly, we developed a content-motion-aware motion estimation based on the motion-level detection. The proposed motion estimation is not limited for FSBM, but valid for all kinds of FBMAs,

## 4. Adaptive Motion Estimation with Variable Subsample Ratios

In the proposed algorithm, we determine the subsample ratio at the beginning of each GOP because the ZMVC of the first interframe prediction is the most accurate. The reference frame in the first interframe prediction is a reconstructed I-frame but others are not for each GOP. Only the reconstructed I-frame does not incur the influence resulted from the quality degradation of the inaccurate interframe prediction. That is, we only calculate the ZMVC of the first P-frame for the subsample ratio selection to efficiently save the computational load of ZMVC. Note that the ZMVC of the first P-frame is calculated by using 16 : 16 subsample ratio. Given the ZMVC of the first P-frame, the motion-level is determined by comparing the ZMVC with preestimated threshold values. The threshold values is decided statistically using popular video clips.

## 5. Selection of ZMVC Threshold and Simulation Results

Testing video sequences.

Video sequence | Number of frames | |
---|---|---|

Fast Motion | Dancer | 250 |

Foreman | 300 | |

Flower | 250 | |

Normal Motion | Table | 300 |

Mother_Daughter (M_D) | 300 | |

Weather | 300 | |

Children | 300 | |

Paris | 300 | |

Slow Motion | News | 300 |

Akiyo | 300 | |

Silent | 300 | |

Container | 300 |

Analysis of quality degradation using three adaptive subsample rate decisions.

Dancer | −0.02 | −0.02 | −0.02 | −0.09 | −0.36 | −0.77 |

Foreman | −0.09 | −0.15 | −0.16 | −0.31 | −0.33 | −0.59 |

Flower | 0 | −0.04 | −0.04 | −0.15 | −0.27 | −0.44 |

Table | −0.05 | −0.06 | −0.11 | −0.19 | −0.26 | −0.34 |

M_D | −0.2 | −0.22 | −0.23 | −0.33 | −0.36 | −0.45 |

Weather | −0.2 | −0.22 | −0.25 | −0.29 | −0.33 | −0.33 |

Children | −0.13 | −0.16 | −0.19 | −0.28 | −0.29 | −0.29 |

Paris | −0.17 | −0.22 | −0.21 | −0.31 | −0.35 | −0.35 |

News | −0.08 | −0.1 | −0.12 | −0.15 | −0.2 | −0.20 |

Akiyo | −0.09 | −0.12 | −0.12 | −0.15 | −0.15 | −0.15 |

Silent | −0.06 | −0.05 | −0.04 | −0.06 | −0.09 | −0.09 |

Container | −0.02 | −0.02 | −0.02 | −0.02 | −0.02 | −0.02 |

Analysis of average subsample ratio using three adaptive subsample rate decisions.

Dancer | 16 : 15.55 | 16 : 15.55 | 16 : 15.55 | 16 : 14.43 | 16 : 11.75 |

Foreman | 16 : 14.32 | 16 : 13.31 | 16 : 12.93 | 16 : 10.61 | 16 : 10.24 |

Flower | 16 : 16.00 | 16 : 15.10 | 16 : 15.10 | 16 : 11.98 | 16 : 8.80 |

Table | 16 : 9.50 | 16 : 9.03 | 16 : 7.17 | 16 : 5.32 | 16 : 4.57 |

M_D | 16 : 7.08 | 16 : 6.43 | 16 : 6.34 | 16 : 3.92 | 16 : 3.55 |

Weather | 16 : 5.87 | 16 : 5.32 | 16 : 4.39 | 16 : 3.18 | 16 : 3.00 |

Children | 16 : 7.82 | 16 : 7.27 | 16 : 6.43 | 16 : 3.83 | 16 : 3.27 |

Paris | 16 : 6.52 | 16 : 6.25 | 16 : 5.22 | 16 : 3.46 | 16 : 3.00 |

News | 16 : 7.45 | 16 : 6.71 | 16 : 4.95 | 16 : 3.09 | 16 : 3.00 |

Akiyo | 16 : 4.76 | 16 : 3.83 | 16 : 3.46 | 16 : 3.00 | 16 : 3.00 |

Silent | 16 : 7.27 | 16 : 7.08 | 16 : 6.34 | 16 : 3.92 | 16 : 3.00 |

Container | 16 : 3.18 | 16 : 3.00 | 16 : 3.00 | 16 : 3.00 | 16 : 3.00 |

Average | 16 : 8.58 | 16 : 8.04 | 16 : 7.35 | 16 : 5.60 | 16 : 4.87 |

Performance analysis of quality degradation for various video sequences using various methods. (Note that the proposed algorithm can always keep the quality degradation low.)

Full search block matching (FSBM) algorithm | |||||||||
---|---|---|---|---|---|---|---|---|---|

Generic | Generic | Generic | Generic | Generic | Generic | Generic | Generic | Proposed | |

Video | 16 : 16 | 16 : 14 | 16 : 12 | 16 : 10 | 16 : 8 | 16 : 6 | 16 : 4 | 16 : 2 | algorithm |

sequence | subsample | subsample | subsample | subsample | subsample | subsample | subsample | subsample | (70%) |

ratio | ratio | ratio | ratio | ratio | ratio | ratio | ratio | ||

PSNRY | |||||||||

Dancer | 34.42 | −0.18 | −0.33 | −0.53 | −0.7 | −0.86 | −0.92 | −0.93 | −0.36 |

Foreman | 30.51 | −0.09 | −0.18 | −0.27 | −0.4 | −0.55 | −0.72 | −0.78 | −0.33 |

Flower | 20.58 | −0.05 | −0.1 | −0.18 | −0.28 | −0.4 | −0.49 | −0.51 | −0.27 |

Table | 32.04 | −0.02 | −0.04 | −0.09 | −0.13 | −0.16 | −0.24 | −0.35 | −0.26 |

M_D | 40.34 | −0.03 | −0.02 | −0.08 | −0.15 | −0.25 | −0.35 | −0.46 | −0.36 |

Weather | 33.26 | −0.06 | −0.1 | −0.09 | −0.15 | −0.22 | −0.28 | −0.33 | −0.33 |

Children | 30 | −0.01 | −0.05 | −0.11 | −0.14 | −0.17 | −0.22 | −0.29 | −0.29 |

Paris | 31.67 | 0 | −0.04 | −0.05 | −0.1 | −0.13 | −0.27 | −0.33 | −0.35 |

News | 38.27 | −0.02 | −0.01 | −0.04 | −0.06 | −0.09 | −0.13 | −0.22 | −0.2 |

Akiyo | 43.36 | 0.01 | −0.01 | −0.02 | −0.03 | −0.05 | −0.09 | −0.16 | −0.15 |

Silent | 35.62 | −0.03 | −0.03 | −0.03 | −0.02 | −0.02 | −0.06 | −0.08 | −0.09 |

Container | 36.47 | 0 | −0.01 | −0.01 | 0 | −0.02 | −0.02 | −0.02 | −0.02 |

Performance analysis of speedup ratio.

Full search block matching (FSBM) algorithm | |||||||||
---|---|---|---|---|---|---|---|---|---|

Generic | Generic | Generic | Generic | Generic | Generic | Generic | Generic | Proposed | |

Video | 16 : 16 | 16 : 14 | 16 : 12 | 16 : 10 | 16 : 8 | 16 : 6 | 16 : 4 | 16 : 2 | algorithm |

sequence | subsample | subsample | subsample | subsample | subsample | subsample | subsample | subsample | (70%) |

ratio | ratio | ratio | ratio | ratio | ratio | ratio | ratio | ratio | |

Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | |

Dancer | 1 | 1.143 | 1.3334 | 1.60011 | 2.0001 | 2.6671 | 4.0006 | 8.0012 | 1.36 |

Foreman | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 1.56 |

Flower | 1 | 1.143 | 1.3334 | 1.60011 | 2.0001 | 2.6671 | 4.0006 | 8.0012 | 1.82 |

Table | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 3.50 |

M_D | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 4.50 |

Weather | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 5.33 |

Children | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 4.89 |

Paris | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 5.33 |

News | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 5.33 |

Akiyo | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 5.33 |

Silent | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 5.33 |

Container | 1 | 1.143 | 1.3334 | 1.60013 | 2.0002 | 2.6669 | 4.0003 | 8.0006 | 5.33 |

Performance analysis of quality degradation for various video sequences using various methods. (Note that the proposed algorithm can always keep the quality degradation low.)

Fast motion estimation (FME) algorithm | |||||||||
---|---|---|---|---|---|---|---|---|---|

Generic | Generic | Generic | Generic | Generic | Generic | Generic | Generic | Proposed | |

Video | 16 : 16 | 16 : 14 | 16 : 12 | 16 : 10 | 16 : 8 | 16 : 6 | 16 : 4 | 16 : 2 | algorithm |

sequence | subsample | subsample | subsample | subsample | subsample | subsample | subsample | subsample | (70%) |

ratio | ratio | ratio | ratio | ratio | ratio | ratio | ratio | ||

PSNRY | |||||||||

Dancer | 33.48 | −0.17 | −0.31 | −0.47 | −0.63 | −0.84 | −1.01 | −0.99 | −0.05 |

Foreman | 29.63 | −0.06 | −0.11 | −0.17 | −0.21 | −0.29 | −0.45 | −0.69 | −0.08 |

Flower | 19.64 | −0.01 | −0.03 | −0.06 | −0.08 | −0.15 | −0.25 | −0.48 | −0.01 |

Table | 31.07 | −0.02 | −0.03 | −0.06 | −0.07 | −0.11 | −0.17 | −0.25 | −0.09 |

M_D | 39.44 | 0 | 0 | −0.02 | −0.02 | −0.05 | −0.12 | −0.31 | −0.24 |

Weather | 32.34 | −0.01 | −0.02 | −0.05 | −0.09 | −0.07 | −0.13 | −0.27 | −0.26 |

Children | 29.12 | −0.06 | −0.08 | −0.02 | −0.15 | −0.16 | −0.23 | −0.3 | −0.27 |

Paris | 30.69 | 0.04 | 0.02 | 0.04 | 0.04 | 0.01 | −0.05 | −0.21 | −0.15 |

News | 37.29 | 0.03 | 0.05 | 0.03 | 0.05 | 0.05 | 0.03 | −0.05 | −0.05 |

Akiyo | 42.38 | 0.03 | 0.04 | 0.03 | 0.02 | −0.01 | −0.02 | −0.07 | −0.08 |

Silent | 34.64 | 0 | 0 | 0 | 0 | 0.04 | 0.05 | 0.02 | 0 |

Container | 35.5 | 0 | 0.02 | 0.01 | 0 | 0 | 0.01 | −0.03 | −0.02 |

Performance analysis of speedup ratio.

Fast motion estimation (FME) algorithm | |||||||||
---|---|---|---|---|---|---|---|---|---|

Generic | Generic | Generic | Generic | Generic | Generic | Generic | Generic | Proposed | |

Video | 16 : 16 | 16 : 14 | 16 : 12 | 16 : 10 | 16 : 8 | 16 : 6 | 16 : 4 | 16 : 2 | Algorithm |

sequence | subsample | subsample | subsample | subsample | subsample | subsample | subsample | subsample | (70%) |

ratio | ratio | ratio | ratio | ratio | ratio | ratio | ratio | ratio | |

Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | Speedup | |

Dancer | 1 | 1.147252 | 1.346325 | 1.626553 | 2.051174 | 2.768337 | 4.208802 | 8.5202 | 1.056017 |

Foreman | 1 | 1.14796 | 1.34685 | 1.6294 | 2.05981 | 2.78782 | 4.25265 | 8.2275502 | 1.16797 |

Flower | 1 | 1.143542 | 1.335488 | 1.603778 | 2.006855 | 2.63666 | 3.975399 | 8.096571 | 1.061454 |

Table | 1 | 1.150301 | 1.352315 | 1.637259 | 2.067149 | 2.7824 | 4.210231 | 8.497531 | 2.50664 |

M_D | 1 | 1.150295 | 1.349931 | 1.627438 | 2.040879 | 2.724727 | 4.086932 | 8.16456 | 4.611836 |

Weather | 1 | 1.153651 | 1.36162 | 1.653674 | 2.092012 | 2.815901 | 4.250473 | 8.529343 | 5.379942 |

Children | 1 | 1.219562 | 1.488654 | 1.719515 | 2.569355 | 3.51429 | 5.697292 | 12.43916 | 5.056478 |

Paris | 1 | 1.15079 | 1.354444 | 1.645437 | 2.083324 | 2.812825 | 4.270938 | 8.627448 | 5.422681 |

News | 1 | 1.150716 | 1.351302 | 1.631096 | 2.04845 | 2.740255 | 4.12047 | 8.253857 | 5.260884 |

Akiyo | 1 | 1.145874 | 1.340152 | 1.61157 | 2.017577 | 2.692641 | 4.04448 | 8.080182 | 5.35473 |

Silent | 1 | 1.15267 | 1.355195 | 1.63785 | 2.060897 | 2.7634 | 4.160839 | 8.338212 | 4.845362 |

Container | 1 | 1.149457 | 1.348652 | 1.626109 | 2.0412 | 2.731408 | 4.109702 | 8.226404 | 5.428775 |

## 6. Conclusion

In this paper, we present a quality-stationary ME that is aware of content motion. By setting the subsample ratio according to the motion-level, the proposed algorithm can have the quality degradation low all over the video frames and require low computation load. As shown in the experimental results, with the optimal threshold values, the algorithm can make the quality degradation less than 0.36 dB while saving 69.6% ( )) power consumption for FSBM. For the application of FBMA, the quality is stationary with the degradation of 0.27 dB and the power consumption is reduced by the factor of 62.2% ( )). The estimation of power consumption reduction is referred to the average subsampling ratio in that the power consumption should be proportional to the subsampling amount. The higher the subsampling amount, the more the power consumption. One can also adjust the size of search range or calculation precision for achieving the quality-stationary. However, either approach cannot have high degree of flexibility for hardware implementation.

## Declarations

### Acknowledgment

This work was supported in part by the National Science Council, R.O.C., under the grant number NSC 95-2221-E-009-337-MY3.

## Authors’ Affiliations

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