# Multiresolution based hierarchical disparity estimation for stereo image pair compression

01 Jan 1994-

TL;DR: A multiresolution based approach is proposed for compressing 'still' stereo image pairs and the typical computational gains and compression ratios possible with this approach are provided.

Abstract: Stereo vision is the process of viewing two different perspective projections of the same real world scene and perceiving the depth that was present in the original scene. These projections offer a compact 2-dimensional means of representing a 3-dimensional scene, as seen by one observer. Different display schemes have been developed to ensure that each eye sees the image that is intended for it. Each image in the image pair is referred to as the left or right image depending on the eye it is intended for. The binocular cues contain unambiguous information in contrast to monocular cues like shading or coloring. Hence binocular stereo may be quite useful, for instance, in video based training of personnel. On the entertainment side, it can make mundane TV material lively. Though the concept has been around for more than half a century, only recently have technically effective ways of making stereoscopic displays and the usually required eyeware emerged. Despite this progress, stereo TV can be made a cost effective add-on option only if the increased bandwidth requirement is relaxed somehow. Since the two images are projections of the same scene from two nearby points of view, they are bound to have a lot of redundancy between them. By properly exploiting this redundancy, the two image streams might be compressed and transmitted through a single monocular channel's bandwidth. The first step towards stereoscopic image sequence compression is 'still' stereo image pair compression that exploits the high correlation between the left and right images, in addition to exploiting the spatial correlation within each image. The temporal correlation between the frames can be taken advantage of, along the lines of the MPEG (Motion Picture Experts Group) standards, to achieve further compression. The final step would be to explore the correlation between left and right frames with a time offset between them. In this paper a multiresolution based approach is proposed for compressing 'still' stereo image pairs. In Section II the task at hand is contrasted with the stereo disparity estimation problem in the machine vision community; a block based scheme on the lines of a motion estimation scheme is suggested as a possible approach. In Section III, the suitability of hierarchical techniques for disparity estimation is outlined. Section IV provides an overview of wavelet decomposition. Section V details the multiresolution approach taken. In section VI, the typical computational gains and compression ratios possible with this …

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06 Sep 2004TL;DR: A stereoscopic image coder based on the MRF model and MAP estimation of the disparity field that takes into account the residual energy, smoothness constraints and the occlusion field is presented.

Abstract: We present a stereoscopic image coder based on the MRF model and MAP estimation of the disparity field. The MRF model minimizes the noise of the disparity compensation because it takes into account the residual energy, smoothness constraints and the occlusion field. The disparity compensation is formulated as a MAP-MRF problem in the spatial domain and the MRF field consists of the disparity vector and occlusion field, which is partitioned into three regions by an initial double-threshold setting. The MAP search is conducted in a block-based sense on one or two of the three regions, providing faster execution. The reference and the residual images are decomposed by a discrete wavelet transform and the transform coefficients are encoded by employing the morphological representation of wavelet coefficients algorithm. As a result of the morphological encoding, the reference and residual images together with the disparity vector field are transmitted in partitions lowering the total entropy. The experimental evaluation on synthetic and real images shows beneficial performance of the proposed algorithm.

2 citations

### Cites methods from "Multiresolution based hierarchical ..."

...Τhe block-matching algorithm may also be applied on the objects that appear after the object contour extraction in the two images [4] or on the subbands of a wavelet decomposed stereo image pair, in a hierarchical way [5]....

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TL;DR: A new stereo image compression algorithm is described in which the residual image, extracted from the stereo image by the disparity-compensated prediction method, is compressed using the wavelet transform in consideration of the inter and intra correlation between subbands.

Abstract: A new stereo image compression algorithm is described in
which the residual image, extracted from the stereo image by the
disparity-compensated prediction method, is compressed using the
wavelet transform in consideration of the inter and intra correlation between
subbands. Contrasting the conventional algorithms such as efficient
pyramid image code (EPIC), embedded predictive wavelet image
coder (EPWIC), and JPEG, compression performance of the suggested
method is significantly improved through computer simulation. Finally, it
is suggested that the stereo image having a good 3-D effect can be
reconstructed from the compressed image data using a new algorithm
described.

2 citations

01 Jan 2005

TL;DR: Experimental results show that basic block matching gives better results than ground truth, especially on occluded regions and boundaries.

Abstract: In order to compress stereo image pairs effectively, disparity compensation is most widely used. In this paper we examined the performances of using different disparity maps and their properties. These properties include the block size, estimation method and the resulting entropy of the disparity map. Experimental results show that basic block matching gives better results than ground truth, especially on occluded regions and boundaries.

1 citations

### Additional excerpts

...Aykırılık kestirimi, stereo imge kodlamada çok kullanılan bir yöntemdir [1,2]....

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01 Jan 2004

TL;DR: Seven wavelet-based stereo image compression algorithms are proposed, to take advantage of the higher data compaction capability and better flexibility of wavelets.

Abstract: With the standardization of JPEG-2000, wavelet-based image and video
compression technologies are gradually replacing the popular DCT-based methods. In
parallel to this, recent developments in autostereoscopic display technology is now
threatening to revolutionize the way in which consumers are used to enjoying the
traditional 2-D display based electronic media such as television, computer and
movies. However, due to the two-fold bandwidth/storage space requirement of
stereoscopic imaging, an essential requirement of a stereo imaging system is efficient
data compression.
In this thesis, seven wavelet-based stereo image compression algorithms are
proposed, to take advantage of the higher data compaction capability and better
flexibility of wavelets. [Continues.]

##### References

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TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.

Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >

20,028 citations

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Bell Labs

^{1}TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.

Abstract: We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.

8,588 citations

### "Multiresolution based hierarchical ..." refers background in this paper

...The maximum vertical disparity (VDMAX) is within 3-4 pixels for reasonably composed image pairs....

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TL;DR: A technique for image encoding in which local operators of many scales but identical shape serve as the basis functions, which tends to enhance salient image features and is well suited for many image analysis tasks as well as for image compression.

Abstract: We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.

6,975 citations

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01 Jan 1984

TL;DR: A Hierarchical Image Analysis System Based Upon Oriented Zero Crossings of Bandpassed Images and a Tutorial on Quadtree Research.

Abstract: I Image Pyramids and Their Uses.- 1. Some Useful Properties of Pyramids.- 2. The Pyramid as a Structure for Efficient Computation.- II Architectures and Systems.- 3. Multiprocessor Pyramid Architectures for Bottom-Up Image Analysis.- 4. Visual and Conceptual Hierarchy - A Paradigm for Studies of Automated Generation of Recognition Strategies.- 5. Multiresolution Processing.- 6. The Several Steps from Icon to Symbol Using Structured Cone/ Pyramids.- III Modelling, Processing, and Segmentation.- 7. Time Series Models for Multiresolution Images.- 8. Node Linking Strategies in Pyramids for Image Segmentation.- 9. Multilevel Image Reconstruction.- 10. Sorting, Histogramming, and Other Statistical Operations on a Pyramid Machine.- IV Features and Shape Analysis.- 11. A Hierarchical Image Analysis System Based Upon Oriented Zero Crossings of Bandpassed Images.- 12. A Multiresolution Representation for Shape.- 13. Multiresolution Feature Encodings.- 14. Multiple-Size Operators and Optimal Curve Finding.- V Region Representation and Surface Interpolation.- 15. A Tutorial on Quadtree Research.- 16. Multiresolution 3-d Image Processing and Graphics.- 17. Multilevel Reconstruction of Visual Surfaces: Variational Principles and Finite-Element Representations.- VI Time-Varying Analysis.- 18. Multilevel Relaxation in Low-Level Computer Vision.- 19. Region Matching in Pyramids for Dynamic Scene Analysis.- 20. Hierarchical Estimation of Spatial Properties from Motion.- VII Applications.- 21. Multiresolution Microscopy.- 22. Two-Resolution Detection of Lung Tumors in Chest Radiographs.- Index of Contributors.

623 citations