Human visual system weighted progressive image transmission using lapped orthogonal transform/classified vector quantization

Hwang, Chan-Sik; Venkatraman, Suresh; Rao, K. R.

doi:10.1117/12.138508

Cited by 6 publications

(3 citation statements)

References 1 publication

(2 reference statements)

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“…9. It can be clearly seen that subjective quality as well as SNR values (see Table 111) of reconstructed image is superior than that reported in [5].…”

Section: I11 -Simulation Resultsmentioning

confidence: 62%

“…The blocks of LOT coefficients are classified into four perceptual classes: one low activity class and three activity classes corresponding to the three edge orientationshorizontal, vertical and diagonalbased on three activity indices calculated using the energy of certain coefficients [5]. If all the three indices are below an empirical threshold (Th), the block is classified as a low-activity block.…”

Section: Ii) Ac Activity Index Calculationmentioning

confidence: 99%

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An efficient lapped orthogonal transform image coding technique

Kumar

Rathi²,

Jain³

1997

IEEE Trans. Consumer Electron.

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A fast and computationally less complex coding techque is described which uses partial-LOT computation a l g o r i b and efficiently cfiscards perceptually insignificant high frequency transform coefficients. The coding process involves AC energy classification, human visual system weighted normalization and quantization. The values of normalization factors are image independent and governed only by the bit-rate and activity index of the image blocks. Zones of various shapes and sizes are chosen for each activity class to perfom zonal sampling which efficiently cfiscards high frequency coefficients having zero values. A progressive transmission version of the proposed technique is also given. This technique gives better performance at comparable bit-rate than that obtained by using LOTlCVQ and LOTNQ schemes.

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“…9. It can be clearly seen that subjective quality as well as SNR values (see Table 111) of reconstructed image is superior than that reported in [5].…”

Section: I11 -Simulation Resultsmentioning

confidence: 62%

Section: Ii) Ac Activity Index Calculationmentioning

confidence: 99%

An efficient lapped orthogonal transform image coding technique

Kumar

Rathi²,

Jain³

1997

IEEE Trans. Consumer Electron.

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“…Among the ones summarized in [7] were the 2D Discrete Cosine Transform (DCT), implemented either block-wise or on the entire image (i.e. full-frame DCT) [13,14], the Lapped Orthogonal Transform (LOT) [15], as well as other classical methods like vector quantization [16] and adaptive predictive coding [17].…”

Section: D Systemsmentioning

confidence: 99%

Trends in Medical Image Compression

Menegaz¹

2006

CMIR

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This paper presents an overview on the state-of-the-art in the field of medical image coding. After a summary of the more representative 2D and 3D compression algorithms, a versatile model-based coding scheme for threedimensional medical data is introduced. The potential of the proposed system is in the fact that it copes with many of the requirements characteristic of the medical imaging field without sacrificing compression efficiency. Among the most interesting features are progressively refinable up-to-lossless quality of the decoded information, object-based functionalities and the possibility to decode a single 2D image of the dataset. Furthermore, such features can be combined enabling a swift access to any two-dimensional object of any image of interest with refinable quality. The price to pay is a slight degradation of the compression performance. Though, the possibility to focus the decoding process on a specific region of a certain 2D image allows a very efficient access to the information of interest, which can be recovered to the desired up-to lossless quality. This is believed to be an important feature for a coding system meant to be used for medical applications, which largely compensates for the eventual loss in compression that could be implied.

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