A new block-based motion estimation algorithm

Motion estimation is known to be the main bottleneck in real-time encoding applications, and the search for an effective motion estimation algorithm (in terms of computational complexity and compression efficiency) has been a challenging problem for years. This paper describes a new block-matching algorithm that is much faster than the full search algorithm and occasionally even produces better rate-distortion curves than the full search algorithms. We observe that a piecewise continuous motion field reduces the bit rate for differentially encoded motion vectors. Our motion estimation algorithm exploits the spatial correlations of motion vectors effectively in the sense of producing better rate-distortion curves. Furthermore, we incorporate such correlations in a multiresolution framework to reduce the computational complexity. Simulation shows that this method is successful because of the homogeneous and reliable estimation of the displacement vectors. In nine out of our ten benchmark simulations, the performance of the full search algorithm and that of our subblock multiresolution method is about the same. In one out of our ten benchmark simulations, our method has improvement.

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“…The initial search area could be reduced by exploiting corrections of motion vectors between spatial and temporal adjacent blocks [12], [29].…”

Section: A Fast Block-matching Motion Estimation Algorithmsmentioning

confidence: 99%

A fast rate-optimized motion estimation algorithm for low-bit-rate video coding

Chen

Kung

1999

IEEE Trans. Circuits Syst. Video Technol.

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“…Each of two methods has its own strong points and weak points. In our previous paper [5], based on the investigation of the different motion estimation algorithms, a new type block-based motion estimation algorithm was proposed and was successfully applied in motion-compensated predictive coding. The motion estimation algorithm is a hybrid version of the block-recursive method and the block-matching method, and it has advantages of high estimation accuracy and fast convergence speed.…”

Section: Motion Estimation Algorithmmentioning

confidence: 99%

“…The motion estimation algorithm is a hybrid version of the block-recursive method and the block-matching method, and it has advantages of high estimation accuracy and fast convergence speed. For the details of the algorithm and its performance, interested readers can refer to the articles [5,6].…”

Section: Motion Estimation Algorithmmentioning

confidence: 99%

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<title>Estimating motion vectorfields with smoothness constraints</title>

Xie

Eycken

Oosterlinck

1993

Video Communications and PACS for Medical Applications

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Motion estimation is a key element in motion-compensated image coding. In the conventional motion estimation algorithms, only image intensity information is exploited to estimate the motion parameters in an image sequence. Motion estimation is usually obtained by searching a best match of intensity signals between two successive images, and the resultant motion vectorfields may be very noisy and not suitable for some specific applications like interframe interpolation. Some simple post-processing techniques like vector smoothing and filtering on noisy vectorfields are not very efficient to improve the reliability of motion estimation. In this paper, a motion estimation algorithm with vectorfield smoothness constraints is proposed. Instead of searching a best match only on interframe intensity signals, the motion estimator aims to search an optimum on both the match of interframe intensity signals and the similarity (or smoothness) of local motion vectors. The experiments indicate that the motion vectorfields obtained by this method are much smoother and more homogeneous than those by the conventional estimation algorithms. The simulations of motion-compensated interframe interpolation further show that the motion parameters estimated by the proposed algorithm are reliable and closely approximate to the real physical motion in the scenes.

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“…This approach was described in [1], 121 and is extended here to zoom motion. The proposed neural network method produces PSNRs 4 to 11 dB higher than the full search method and 3 to 6 dB higher than some very recent algorithms [3], with concomitant higher compression ratios.…”

Section: Introductionmentioning

confidence: 92%

Formative motion estimation for translational, shear and zoom sequences

Skrzypkowiak

Jain

1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings

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Digital television seeks to reduce the bit rate through the elimination of interframe redundancy. The more accurate the motion estimation, the lower the encoded bit rate with the same picture quality, resulting in more channels the provider can deliver to the consumer at a reduced price. In this paper we develop a neural network motion estimation technique which produces 4 to 11 dB improvement of Peak Signal to Noise Ratio (PSNR) over the other methods, thus creating the potential for improved compression. In contradistinction to the other methods, there is no need to carry out fractionalpixel interpolation. The technique is applicable to purely translational, shear, and zoom sequences. Further, it is formative in that it can form objects that are formed in the present frame and are non-existent in the previous frame.

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A new block-based motion estimation algorithm

Cited by 12 publications

References 14 publications

A fast rate-optimized motion estimation algorithm for low-bit-rate video coding

A fast rate-optimized motion estimation algorithm for low-bit-rate video coding

<title>Estimating motion vectorfields with smoothness constraints</title>

Formative motion estimation for translational, shear and zoom sequences

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