An Adaptive Algorithm for Motion Compensated Color Image Coding

Kwatra, S.C.; Lin, Chow-Ming; Whyte, Wayne A.

doi:10.1109/tcom.1987.1096840

Cited by 35 publications

(9 citation statements)

References 11 publications

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“…In Ref. 8, the set of thresholds is introduced to determine the motion activity of a block and decide whether to perform the next step of TSS or to finish.…”

Section: Characterization Of Algorithmsmentioning

confidence: 99%

Block-based motion estimation algorithms — a survey

Jakubowski

Pastuszak

2013

Opto-Electronics Review

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In the multi-view video coding, both temporal and inter-view redundancies can be exploited by using standard block-based motion estimation (BBME) technique. In this paper, an extensive review of BBME algorithms proposed within the last three decades is presented. Algorithms are divided into five categories: 1) based on the search position number reduction; 2) multiresolution; 3) based on the simplification of matching criterion; 4) fast full search; 5) computation-aware. Algorithms are compared in terms of their efficiency and computational complexity.

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“…In Ref. 8, the set of thresholds is introduced to determine the motion activity of a block and decide whether to perform the next step of TSS or to finish.…”

Section: Characterization Of Algorithmsmentioning

confidence: 99%

Block-based motion estimation algorithms — a survey

Jakubowski

Pastuszak

2013

Opto-Electronics Review

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“…Thus, the higher the amount of noise in a particular frame, the less trustworthy the frame is, and is therefore weighted less. In this paper, is chosen to be inversely proportional to the magnitude squared of the displaced frame difference (DFD), given by (13) According to this choice of the frames for which accurate motion compensation is achieved are trusted more. If additive noise is present in the data the 's are made inversely proportional to the sum of the 's in (4) and .…”

Section: Iterative Algorithmmentioning

confidence: 99%

“…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. It was found experimentally that, on the average, composite coding (using luminance and chrominance components) resulted in a savings of 11.6% in bit rate compared to luminance coding alone, for the same signal to noise ratio (SNR).…”

Section: A Advantages Of Using Color Sequencesmentioning

confidence: 99%

“…Particular attention is paid to the use of the color channels in estimating the motion. The main premise behind using all of the color channels in motion estimation is that together they yield more accurate motion estimates [13]. This was demonstrated in [14], where lower bit rates of a color sequence could be achieved if color motion estimation was used.…”

Section: Introductionmentioning

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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“…A small difference indicates a larger uncertainty (i.e., the slope of the surface is small and the probability to get a global minimum decreases) and hence the search scope will be increased in the next step. In another method the minimum BDM at each step is compared with the predetermined threshold and the search stops if the BDM value is less than the threshold (Kwatra et al, 1987). Thus these algorithms employ multiple thresholds to control the search and these thresholds play an important role on the performance of the algorithm (Feng et al, 1998).…”

Section: Introductionmentioning

confidence: 99%