Coding image sequence intensities along motion trajectories using EC-CELP quantization

Foodeei, N.; Dubois, Éric

doi:10.1109/icip.1994.413409

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…While this may be sufficient for low-resolution imagery transmitted over the Internet (e.g., video clips from news articles), it is insufficient for applications that require high-quality image representation (for example, detection of small targets in surveillance imagery, medical image-based diagnosis, digital movie distribution, or large-format videoconferencing). As intuitively expected, second-order motion models that represent both velocity and acceleration can more faithfully represent 3-D motion projected to the 2-D camera plane, and tend to support more accurate motion-compensated predictive coding [15,16]. For example, instantaneous velocity x′ and acceleration x ′ ′ can be combined to represent motion of a 2-D point x in a finite image domain X, as follows:…”

Section: Motion Fieldsmentioning

confidence: 95%

Detection and Characterization of Motion in Video Compression

Schmalz

Ritter

2003

SPIE Proceedings

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The movement of objects in video sequences comprises a type of spatiotemporal redundancy that can be decreased mathematically to facilitate video compression. This observation holds particularly in the case of periodic motion, for example, bipedal or quadrupedal locomotion or repetitive gestures. Previously-published motion detection techniques were based on optical flow, interframe differences represented in terms of transform coefficient perturbations, or changes in eigenvalues between frames in a video sequence. However, such methods have deficits that include sensitivity to noise, burdensome computational requirements (e.g., floating point operations), and prohibitive instability in the presence of spatial or temporal interframe discontinuities.In this paper, we discuss several techniques of motion detection in two-dimensional images of three-dimensional scenespointwise tracking of constant-intensity pixels, region-based vector field characterization of apparent motion, and correlationbased detection. In the latter category is a technique called Interframe Similarity Matrices (ISMs). ISMs were developed and successfully applied by Yacoob, Black, and Davis [1,2] to address the challenging problem of detecting human and animal motion in surveillance video sequences. In particular, given an N-frame video sequence, an NxN-element interframe correlation matrix can be constructed and Fourier-transformed to obtain an N/2-element power spectrum of interframe periodicities. Different actions (e.g., walking vs. running) and various actors (e.g., quadruped versus human) tend to be characterized by distinct spatiotemporal spectra, and can often be distinguished from one another.Since each spectrum can be computed from a sequence of small image regions, it is possible to represent interframe motion by a pixel tagging technique, thus implementing detection, segmentation, and representation. If there are K objects with M pixels per frame having B bits per pixel (bpp) in N frames of a compressed video sequence, and each object is segmented into a region represented by a P bit tag, then increased compression results if NKMB < NKP, i.e., MB < P. Implementational discussion concerns efficient algorithms for tagging of motion-containing regions, to decrease the representational overhead to several bits per region. We also discuss motion encoding in a compressed format for purposes of efficient extraction of motion parameters from a compressed image, which can support efficient object recognition in highresolution compressed image sequences.

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Section: Motion Fieldsmentioning

confidence: 95%

Detection and Characterization of Motion in Video Compression

Schmalz

Ritter

2003

SPIE Proceedings

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“…In [5], CELP is extended to two-dimensional (2-D) for image coding. In [6], a CELP-like algorithm is proposed for quantization of image intensities along motion trajectories. We next use the code excited ABS paradigm to derive a vector quantized pel-recusive motion compensation (CEPER) algorithm .…”

Section: Code Excited Pel-recursive Motion Compensationmentioning

confidence: 99%

“…Let be the location of the th pixel in the th block. For that pixel, the quantized DFD value is ; the reconstructed pixel intensity value is (6) and the reconstruction error is (7) By replacing in (1) and (5) with , we obtain the used in (6) and (7), and , respectively. The total reconstruction error of the block due to using code vector is (8) Fig.…”

Section: A Ceper Algorithmmentioning

confidence: 99%

A novel code excited pel-recursive motion compensation algorithm

Shen

Chan²

2001

IEEE Signal Process. Lett.

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The conventional scalar-quantizer-based pel-recursive motion compensated coding scheme is enhanced by incorporating vector quantization. The enhanced scheme uses code-excited analysis-by-synthesis to quantize the motion compensated prediction residual. Simulation results show that the enhanced scheme furnishes large rate-distortion performance gains over the conventional scheme, particularly at bit rates below about 0.5 bits per pixel.

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“…Correct motion information is a very reliable reference for inter-frame prediction. By identifying "corresponding data blocks" along the video stream [2] or some other representation of video stream [3], motion estimation reveals the varying regularity of scenes, which is required for compact coding. Hence, motion estimation has been intergraded into H.261, H.263, and MPEG standards as a significant component.…”

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confidence: 99%