Sub-band coding of images

Woods, John W.; O'Neil, Sean

doi:10.1109/icassp.1986.1168863

Cited by 96 publications

(35 citation statements)

References 8 publications

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“…In fact, we will show in the last section that the quadrature mirror filter (QMF) concept offers a simple solution for dealing with this redundancy. We will thereby also establish the relationship between the present approach and recent work in orthogonal pyramid structures [1,12], wavelet transforms [9] and subband coding techniques [ 18,20].…”

Section: W(z) + W(-z)mentioning

confidence: 87%

“…The two attractive features of this technique are (i) the reversibility of the process (error free reconstruction) and (ii) the fact that the resulting signal decomposition uses no more samples than the initial representation. Recently, several authors have applied this concept to pyramid image compression and have reported substantial improvements in performance [1,12,18,20]. QMF banks also provide an efficient way of computing wavelet transforms, as has been shown recently by Mallat and Daubechies [5,8,9].…”

Section: Link With Qmf Pyramidsmentioning

confidence: 93%

“…This approach compares favorably with earlier techniques, such as transform or predictive image coding, especially when large compression ratios are desired [7]. Recent developments in pyramid image compression also include subband coding techniques [ 18,20], orthogonal pyramid structures [1,12] and wavelet transforms [9], which are all based on the concept of quadrature mirror filters (QMF) [4].…”

Section: Introductionmentioning

confidence: 99%

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An improved least squares Laplacian pyramid for image compression

Unser

1992

Signal Processing

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Abstract. This paper describes two ways of improving Burt and Adelson's Laplacian pyramid, a technique developed for image compression. The Laplacian pyramid is a multi-resolution image representation that captures the loss of information occurring through repeated reduction of the spatial resolution. The generation of this data structure involves the use of two complementary functions: EXPAND, which increases the size of an image by a factor of 2, and REDUCE, which performs the reverse operation. The first modification is the adjunction of a pre-filter to the initial EXPAND function in order to guarantee an image extrapolation that is an exact interpolation of the coarser resolution level. The second refinement is a REDUCE operation modified to minimize information loss. The corresponding least squares Laplacian pyramid (LSLP) is generated by adding a post-filter to the initial REDUCE function. These new functions have an efficient implementation using recursive algorithms. Preliminary experiments indicate improved performance: for a Gaussian-like kernel (a = 3), the new EXPAND function exhibits a 2 to 2.5 dB attenuation of the first level of the Laplacian pyramid, while the complete scheme (LSLP) leads to a 4.7 to 8.5 dB improvement in the two images used to test the procedure. For comparable compression ratios, the subjective image quality for the LSLP appears to be significantly better. A theoretical relationship between the present approach and the family of quadrature mirror filter image pyramids is also derived.Zusammenfassung. Diese Arbeit beschreibt zwei Methoden, die Laplacepyramide yon Burr und Adelson zu verbessern, welche zur Bildkompression dient. Die Laplacepyramide ist eine Bilddarstellung dutch Mehrfachaufl6sung, welche den Informationsverlust erfasst, der durch die wiederholte Reduktion der rS.umlichen Aufl6sung entsteht. Fiir die Erzeugung dieser Datenstruktur werden zwei komplement/ire Funktionen gebraucht : EXPAND, welche das Bild um einen Faktor zwei vergr6s-sert, und REDUCE, welche die inverse Operation durchfiihrt. Die erste Modifikation besteht im Hinzufiigen eines Vorfilters zur EXPAND Funktion, um eine Bildextrapolation zu erreichen, welche eine pr/izise Interpolation des gr6beren Aufl6sungsni-veaus ist~ Die zweite Verbesserung betrifft die REDUCE Funktion, welche modifiziert wird, um einen minimalen Informationsverlust zu erreichen. Die entsprechende Laplacepyramide kleinster Quadrate (LSLP) wird durch Hinzufiigen eines Nachfilters zur REDUCE Funktion erzeugt. Diese zwei neuen Funktionen erlauben eine effiziente Realisierung mithilfe rekursiver Algorithmen. Erste Versuche deuten auf eine deutliche Verbesserung hin: fiir einen Gauss-artigen Kern (a = 3) erreicht die neue EXPAND Funktion eine D/impfung von 2 bis 2.5 dB im ersten Niveau der Laplacepyramide, w/ihrend die vollst/indige Methode (LSLP) eine Verbesserung yon 4.7 bis 8.5 dB fiir zwei Testbilder erreicht. Fiir vergleichbare Bildkompressionsfaktoren ist die subjektive Bildqualit/it von LSLP wesentlich besser. Ferner wird ein the...

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Section: W(z) + W(-z)mentioning

confidence: 87%

Section: Link With Qmf Pyramidsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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An improved least squares Laplacian pyramid for image compression

Unser

1992

Signal Processing

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“…Image coding using wavelets has been attempted by Antonini et al [1], who used biorthogonal wavelets to obtain a set of bi-orthogonal sub bands. Woods and O'Neil [4] extended sub band decomposition to two-dimensional (2-D) signals and proposed a method for QMF design that eliminates possible aliasing error due to non ideal sub band filters. The original image was decomposed at different scales or sub bands of frequency using Mallat's pyramidal architecture [2], in which horizontal and vertical orientations are considered preferential.…”

Section: Introductionmentioning

confidence: 99%

A Neuro Fuzzy Model for Image Compression in Wavelet Domain

Singh

Rajpal

Murthy

2008

Lecture Notes in Computer Science

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Abstract. Image compression forms the backbone for several applications such as storage of images in a database, picture archiving, TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in data present within an image. This work evaluates the performance of an image compression system based on fuzzy vector quantization, wavelet based sub band decomposition and neural network. Vector quantization is often used when high compression ratios are required. The implementation consists of three steps. First, image is decomposed into a set of sub bands with different resolution corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. At the second step, the wavelet coefficients corresponding to lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients corresponding to higher frequency bands are compressed using neural network. The result of the second step is used as input to fuzzy vector quantizer. Image quality is compared objectively using mean squared error and PSNR along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality as compared to other image compression techniques.

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“…ious powerful source coding techniques, discrete cosine transform (DCT) based coding (Rao and Yip 1990), subband/wavelet coding (Woods and O'Neil 1986, Antonini et al1992, Vaidyanathan 1993, Vetteli and Kovacevic 1995, and/or psychophysical-and psychoacoustical-model based perceptual coding (Pan 1995, ISO/IEC 1993, Jayant et al1993) have been widely viewed as the mainstream of compression technology and exploited in multiple international compression standards such as JPEG , H.261 , H.263, MPEG/Video, and MPEG/ Audio for various speech, image, video, and audio applications (Bhaskaran andKonstantinides 1996, Netravali andHaskell 1995).…”

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confidence: 99%

Practical Issues of Dynamic Bit Allocation in Multimedia Source Compression

Mital

1996

Advanced IT Tools

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Owing to various emerging multimedia applications which create enormous amount of data traffic over limited channel bandwidth, dynamic bit allocation plays a significant role on achieving robust and/or optimum source coding performance. In this paper, we focus on pixel-based and perceptual-based dynamic bit allocations which have been widely exploited in many source coding algorithms and international audio)visual compression standards as well. Many fundamental facts of human auditory system have been effectively exploited in perceptual-based bit allocation for speech and audio signals. On the contrary, pixelbased bit allocation which is widely used in image and video coding has certain practical issues required to be addressed. We highlight these observed concerns, present an unified and optimum solution, and recommend a future direction.

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Sub-band coding of images

Cited by 96 publications

References 8 publications

An improved least squares Laplacian pyramid for image compression

An improved least squares Laplacian pyramid for image compression

A Neuro Fuzzy Model for Image Compression in Wavelet Domain

Practical Issues of Dynamic Bit Allocation in Multimedia Source Compression

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