Priority DCT coding for image sequences

Huang, Yicong; Galatsanos, N.P.; Dreizen, Howard M.

doi:10.1109/icassp.1991.150941

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…It is clear that, the quality of the reconstructed image is far superior when the MC approach is used. The results compare very favorably with those presented in previous work, e.g.. in [8,13]. Finally, in Table 1 the average results computed over the entire image sequence are presented.…”

Section: Mnk>[fk(r) -Fk(r)]2supporting

confidence: 75%

“…It was shown that Hppc(X1) + Hppc(I) = H(X1) , (8) where Hppc(Ij) is the entropy of the position information, Hppc(X) is the average entropy of the source in all passes, and H(X1) is the entropy of the original source. Experiments using Arithmetic coding [19] were also performed where it was verified that the average codeword length necessary for coding PPC(X1) and PPC(I2) of all passes is equal to the entropy H(X2).…”

Section: Partition Priority Coding (Ppc)mentioning

confidence: 99%

“…However, the MC coding methods proposed in the literature do not achieve efficient spatio-temporal redundancy reductioi since they use MC prediction of the working frame either at the pixel level or in small blocks. On the other hand, in [8], the removal of the spatio-temporal reduction, simultaneously, on the entire frame was proposed but using a zero-order temporal prediction (repetition of the previous frame) and performing coding of the prediction error (difference of successive frames). Although, this method utilizes the amount of spatia.l correlation in the prediction error image, it does not completely utilize the large amount of temporal correlation along the motion trajectories of the moving scene using MC.…”

Section: Introductionmentioning

confidence: 98%

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<title>Motion-compensated priority discrete cosine transform coding of image sequences</title>

Efstratiadis¹,

Huang

Xiong

et al. 1991

SPIE Proceedings

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In this paper a new motion compensated (MC) predictive coding method for image sequences is presented. This method utilizes a prediction of the displacement vector field (DVF) in order to produce a MC prediction error which is coded and transmitted using the Discrete Cosine Transform (DCT) and the Partition Priority Coding (PPC) approach. Assuming that two previous frames are available both at the transmitter and the receiver, the DVF corresponding to the previous frame is estimated. Then, based on this estimate a temporal prediction of the DVF at the current frame is obtained. This way there is no need to transmit the DVF. Using the predicted DVF, the 4C prediction error is obtained. It is then transformed and the irrelevancy reduction is carried out using the PPC method. According to this method, the transform coefficients are ordered based on their magnitude and, therefore, magnitude and location information is transmitted. Due to the properties of the MC prediction error, PPC proves to be very suitable. The proposed algorithm was experimentally tested on standard video-conferencing image sequences. Significantly improved results were obtained compared to previously reported DCT and Priority DCT based methods without MC in terms of reduced bit-rate and quality of the reconstructed image sequence.

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Section: Mnk>[fk(r) -Fk(r)]2supporting

confidence: 75%

Section: Partition Priority Coding (Ppc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

<title>Motion-compensated priority discrete cosine transform coding of image sequences</title>

Efstratiadis¹,

Huang

Xiong

et al. 1991

SPIE Proceedings

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show abstract

“…Note that, X coded in pass S will obviously not be coded again, and L(Ii) and can be inferred since it includes all X not coded in previous passes. It was shown that [12,13] H(X) + H(I) = H(X), 11,41,6,15,24,3,21,9, where H(I) is the entropy of the position information, H(X) is the average entropy of the source in all passes, and H(X) is the entropy of the original source. Thus, PPC allows the transmission by symbol priority without any additional coding overhead.…”

Section: Partition Priority Codingmentioning

confidence: 99%

“…By considering a partitioning of the range of the quantized coefficients, they are grouped and transmitted based on their magnitude along with their address information. This method has been applied to the coding of BDCT coefficients using multiple distribution entropy coding (MDEC) with very good performance [12,13,14]. In [ 15], Shapiro applies a similar idea to hierarchical image coding and progressive transmission using hierarchical entropy-coded quantization and interscale prediction for exploiting the self-similarity inherent in images.…”

Section: Introductionmentioning

confidence: 99%

<title>Image compression using subband/wavelet transform and adaptive multiple-distribution entropy coding</title>

1992

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Image compression methods using subband/wavelet transform and adaptive multiple distribution entropy coding (AMDEC) are presented. The methods are suitable for the coding of still images, as well as, motion compensated prediction error images for video compression. First, the subband/wavelet transform coefficients are uniformly quantized. For still image coding perception based weighted quantization is used. Space filling scanning, subband partitioning and classification methods are used in dividing the original source of quantized coefficients into a number of subsources with corresponding distributions. A hierarchical partition priority coding (PPC) approach is followed, that is, given a suitable partitioning of their range, the transform coefficients are ordered based on their magnitude. AMDEC is applied to the output of PPC, which contains magnitude and location information, using adaptive arithmetic coding based on the histograms of the various subsources. Experimental results on standard monochromatic images and video-conference image sequences demonstrate the great performance of the proposed AMDEC methods.

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Lossy source coding

Berger

Gibson

1998

IEEE Trans. Inform. Theory

156

150

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Lossy coding of speech, high-quality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called rate-distortion theory. For the first 25 years of its existence, rate-distortion theory had relatively little impact on the methods and systems actually used to compress real sources. Today, however, rate-distortion theoretic concepts are an important component of many lossy compression techniques and standards. We chronicle the development of rate-distortion theory and provide an overview of its influence on the practice of lossy source coding.

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Priority DCT coding for image sequences

Cited by 7 publications

References 10 publications

<title>Motion-compensated priority discrete cosine transform coding of image sequences</title>

<title>Motion-compensated priority discrete cosine transform coding of image sequences</title>

<title>Image compression using subband/wavelet transform and adaptive multiple-distribution entropy coding</title>

Lossy source coding

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