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Author

A. Jain

Bio: A. Jain is an academic researcher. The author has contributed to research in topics: Motion estimation & Motion compensation. The author has an hindex of 1, co-authored 1 publications receiving 1867 citations.

Papers
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Journal ArticleDOI
TL;DR: The motion compensation is applied for analysis and design of a hybrid coding scheme and the results show a factor of two gain at low bit rates.
Abstract: A new technique for estimating interframe displacement of small blocks with minimum mean square error is presented. An efficient algorithm for searching the direction of displacement has been described. The results of applying the technique to two sets of images are presented which show 8-10 dB improvement in interframe variance reduction due to motion compensation. The motion compensation is applied for analysis and design of a hybrid coding scheme and the results show a factor of two gain at low bit rates.

1,883 citations


Cited by
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Journal ArticleDOI
TL;DR: Experimental results show that the proposed diamond search (DS) algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.
Abstract: Based on the study of motion vector distribution from several commonly used test image sequences, a new diamond search (DS) algorithm for fast block-matching motion estimation (BMME) is proposed in this paper. Simulation results demonstrate that the proposed DS algorithm greatly outperforms the well-known three-step search (TSS) algorithm. Compared with the new three-step search (NTSS) algorithm, the DS algorithm achieves close performance but requires less computation by up to 22% on average. Experimental results also show that the DS algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.

1,949 citations

Journal ArticleDOI
TL;DR: Simulation results show that, as compared to TSS, NTSS is much more robust, produces smaller motion compensation errors, and has a very compatible computational complexity.
Abstract: The three-step search (TSS) algorithm has been widely used as the motion estimation technique in some low bit-rate video compression applications, owing to its simplicity and effectiveness. However, TSS uses a uniformly allocated checking point pattern in its first step, which becomes inefficient for the estimation of small motions. A new three-step search (NTSS) algorithm is proposed in the paper. The features of NTSS are that it employs a center-biased checking point pattern in the first step, which is derived by making the search adaptive to the motion vector distribution, and a halfway-stop technique to reduce the computation cost. Simulation results show that, as compared to TSS, NTSS is much more robust, produces smaller motion compensation errors, and has a very compatible computational complexity. >

1,689 citations

Journal ArticleDOI
TL;DR: Simulation results show that the proposed 4SS performs better than the well-known three- step search and has similar performance to the new three-step search (N3SS) in terms of motion compensation errors.
Abstract: Based on the real world image sequence's characteristic of center-biased motion vector distribution, a new four-step search (4SS) algorithm with center-biased checking point pattern for fast block motion estimation is proposed in this paper. A halfway-stop technique is employed in the new algorithm with searching steps of 2 to 4 and the total number of checking points is varied from 17 to 27. Simulation results show that the proposed 4SS performs better than the well-known three-step search and has similar performance to the new three-step search (N3SS) in terms of motion compensation errors. In addition, the 4SS also reduces the worst-case computational requirement from 33 to 27 search points and the average computational requirement from 21 to 19 search points, as compared with N3SS.

1,619 citations

Journal ArticleDOI
TL;DR: First, the concept of vector quantization is introduced, then its application to digital images is explained, and the emphasis is on the usefulness of the vector quantification when it is combined with conventional image coding techniques, orWhen it is used in different domains.
Abstract: A review of vector quantization techniques used for encoding digital images is presented. First, the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis is on the usefulness of the vector quantization when it is combined with conventional image coding techniques, or when it is used in different domains. >

1,102 citations

Patent
03 Jan 1992
TL;DR: In this paper, a system of distributing video and audio information employs digital signal processing to achieve high rates of data compression, and the compressed and encoded audio and video information is sent over standard telephone, cable or satellite broadcast channels to a receiver specified by a subscriber of the service, preferably in less than real time, for later playback and optional recording on standard audio and/or video tape.
Abstract: A system of distributing video and/or audio information employs digital signal processing to achieve high rates of data compression. The compressed and encoded audio and/or video information is sent over standard telephone, cable or satellite broadcast channels to a receiver specified by a subscriber of the service, preferably in less than real time, for later playback and optional recording on standard audio and/or video tape.

1,032 citations