Motion Compensated Component Color Coding

Prabhu, K.; Netravali, Arun N.

doi:10.1109/tcom.1982.1095443

Cited by 15 publications

(3 citation statements)

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“…In general, it was found in [17] that it was sufficient to estimate the motion field of a color sequence based on the luminance component only, and use these parameters for motionbased prediction of both the luminance as well as the chrominance components. In [13], it was found that performing motion estimation using both the luminance and chrominance channels yielded savings in bit rate over that of using motion parameters estimated from the luminance channel alone.…”

Section: A Advantages Of Using Color Sequencesmentioning

confidence: 99%

“…The second set of experiments focused on the role of the motion estimator by comparing the results obtained from a block matching algorithm with that of a pel-recursive approach. Results were measured by finding the peak signal to noise ratio (PSNR), which was calculated according to (17) where are the original and estimated high-resolution frames, respectively, and denote the dimensions of each frame. Finally, the step size difference was used as the convergence criterion, and the algorithm was terminated when this value reached .…”

Section: A Experimental Setupmentioning

confidence: 99%

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Resolution enhancement of monochrome and color video using motion compensation

Tom

Katsaggelos

2001

IEEE Trans. on Image Process.

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We propose an iterative algorithm for enhancing the resolution of monochrome and color image sequences. Various approaches toward motion estimation are investigated and compared. Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since the original high-resolution images are not available. Improved motion estimates may be obtained by using a more robust and accurate motion estimator, such as a pel-recursive scheme instead of block matching, in processing color image sequences, there is the added advantage of having more flexibility in how the final motion estimates are obtained, and further improvement in the accuracy of the motion field is therefore possible. This is because there are three different intensity fields (channels) conveying the same motion information. In this paper, the choice of which motion estimator to use versus how the final estimates are obtained is weighed to see which issue is more critical in improving the estimated high-resolution sequences. Toward this end, an iterative algorithm is proposed, and two sets of experiments are presented. First, several different experiments using the same motion estimator but three different data fusion approaches to merge the individual motion fields were performed. Second, estimated high-resolution images using the block matching estimator were compared to those obtained by employing a pel-recursive scheme. Experiments were performed on a real color image sequence, and performance was measured by the peak signal to noise ratio (PSNR).

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Section: A Advantages Of Using Color Sequencesmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Resolution enhancement of monochrome and color video using motion compensation

Tom

Katsaggelos

2001

IEEE Trans. on Image Process.

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“…Bell Laboratories has proposed [5][6][7][8][9][10] a PRA that estimates motion on a pixel-by-pixel basis. They reported an improvement of 30% to 60% in bandwidth reduction [5J.…”

Section: Pixel-recursive Algorithm (Pra)mentioning

confidence: 99%

Block-recursive matching algorithm (BRMA) for displacement estimation of viedo images

Tzou¹,

Hsing²,

Daly

1985

ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing

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Contour Image Sequence Compression through Motion Analysis and Hybrid Coding Method

Huang

1992

Multidimensional Processing of Video Signals

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Abstract. This paper presents a motion analysis algorithm (MAA) and a hybrid coding method for contour image sequence compression. The contour image sequence consists of objects moving and rotating in a 3-D world with occlusion, shape, and size variations from frame to frame. The MAA separates the moving image sequence into several object-oriented subsequences (OOSs). In each OOS, the component is either stationary or moves smoothly, and the motion parameters can be easily estimated. The first and last frames of OOS are key frames, and the others are in-between frames. The key frames are unpredictable, and the entire frames need to be encoded. The in-between frames are compensable, and they are encoded by the motion parameter coding. The hybrid coder uses vectorgraph coding to remove spatial redundancy of the key frames and motion parameter coding to reduce the temporal redundancy of the OOSs. The motion parameters are encoded as combinations of 2-D translation, 2-D rotation, and scaling. There are many applications for contour image sequence compression. The cartoon image sequence (a sequence of line drawing sketches) and the high-frame-rate videophone for sign language transmission are good examples. Experiments show that our method encodes the contour image sequence at a very high compression ratio without losing intelligibility.

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Motion Compensated Component Color Coding

Cited by 15 publications

References 4 publications

Resolution enhancement of monochrome and color video using motion compensation

Resolution enhancement of monochrome and color video using motion compensation

Block-recursive matching algorithm (BRMA) for displacement estimation of viedo images

Contour Image Sequence Compression through Motion Analysis and Hybrid Coding Method

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