# 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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TL;DR: A new stereo image coding algorithm that is based on disparity compensation and subspace projection is described, and empirical results suggest that the SPT approach outperforms current stereo coding techniques.

Abstract: Due to advances in display technology, three-dimensional (3-D) imaging systems are becoming increasingly popular. One way of stimulating 3-D perception is to use stereo pairs, a pair of images of the same scene acquired from different perspectives. Since there is an inherent redundancy between the images of a stereo pair, data compression algorithms should be employed to represent stereo pairs efficiently. This paper focuses on the stereo image coding problem. We begin with a description of the problem and a survey of current stereo coding techniques. A new stereo image coding algorithm that is based on disparity compensation and subspace projection is described. This algorithm, the subspace projection technique (SPT), is a transform domain approach with a space-varying transformation matrix and may be interpreted as a spatial-transform domain representation of the stereo data. The advantage of the proposed approach is that it can locally adapt to the changes in the cross-correlation characteristics of the stereo pairs. Several design issues and implementations of the algorithm are discussed. Finally, we present empirical results suggesting that the SPT approach outperforms current stereo coding techniques.

68 citations

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TL;DR: A new fully scalable image coder is presented and the lossless and lossy performance of these transforms in the proposed coder are investigated, which are comparable to JPEG-LS.

Abstract: Reversible integer wavelet transforms allow both lossless and lossy decoding using a single bitstream. We present a new fully scalable image coder and investigate the lossless and lossy performance of these transforms in the proposed coder. The lossless compression performance of the presented method is comparable to JPEG-LS. The lossy performance is quite competitive with other efficient lossy compression methods.

59 citations

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TL;DR: A new stereo image compression scheme that is based on the wavelet transform of both images and the disparity estimation between the stereo pair subbands and demonstrates very good performance as far as PSNR measures and visual quality are concerned and low complexity.

Abstract: In this paper, we propose a new stereo image compression scheme that is based on the wavelet transform of both images and the disparity estimation between the stereo pair subbands. The two images are decomposed by using a Discrete Wavelet Transform (DWT) and coded by employing the morphological representation of the wavelet coefficients, which is a technique that exploits the intraband–interband statistical properties of them. The progressive pixel-to-pixel evaluation of the disparity has been incorporated to the morphological coder so that a dense disparity field to be formed for every subband. The proposed method demonstrates very good performance as far as PSNR measures and visual quality are concerned and low complexity.

44 citations

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TL;DR: In this paper, the authors exploit the correlations between 3D-stereoscopic left-right image pairs to achieve high compression factors for imageframe storage and image stream transmission, and they find extremely high correlations between left- right frames offset in time such that perspective-induced disparity between viewpoints and motion-induced parallax from a single viewpoint are nearly identical; they coin the term "WoridLine correlation" for this condition.

Abstract: We exploit the correlations between 3D-stereoscopic left-right image pairs to achieve high compression factors for imageframe storage and image stream transmission. In particular, in image stream transmission, we can find extremely highcorrelations between left-right frames offset in time such that perspective-induced disparity between viewpoints and motion-induced parallax from a single viewpoint are nearly identical; we coin the term "WoridLine correlation' for this condition.We test these ideas in two implementations, (1) straightforward computing of blockwise cross- correlations, and (2)multiresolution hierarchical matching using a wavelet- based compression method. We find that good 3D-stereoscopic imagery can be had for only a few percent more storage space or transmission bandwidth than is required for the corresponding flat imagery.1. INTRODUCTIONThe successful development of compression schemes for motion video that exploit the high correlation between temporallyadjacent frames, e.g., MPEG, suggests that we might analogously exploit the high correlation between spatially or angularlyadjacent still frames, i.e., left-right 3D-stereoscopic image pairs. If left-right pairs are selected from 3D-stereoscopic motionstreams at different times, such that perspective-induced disparity left-right and motion-induced disparity earlier-laterproduce about the same visual effect, then extremely high correlation will exist between the members of these pairs. Thiseffect, for which we coin the term "WorldLine correlation", can be exploited to achieve extremely high compression factorsfor stereo video streams.Our experiments demonstrate that a reasonable synthesis of one image of a left-right stereo image pair can be estimated fromthe other uncompressed or conventionally compressed image augmented by a small set of numbers that describe the localcross-correlations in terms of a disparity map. When the set is as small (in bits) as 1 to 2% of the conventionally compressedimage the stereoscopically viewed pair consisting of one original and one synthesized image produces convincing stereoimagery. Occlusions, for which this approach of course fails, can be handled efficiently by encoding and transmitting errormaps (residuals) of regions where a local statistical operator indicates that an occlusion is probable.Two cross-correlation mapping schemes independently developed by two of us (P.G. and S.S.) have been coded and tested,

39 citations

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TL;DR: The psychophysical property of the human visual system, that only one high resolution image in a stereo image pair is sufficient for satisfactory depth perception, has been used to further reduce the bit rates in this paper.

Abstract: Stereoscopic sequence compression typically involves the exploitation of the spatial redundancy between the left and right streams to achieve higher compressions than are possible with the independent compression of the two streams. In this paper the psychophysical property of the human visual system, that only one high resolution image in a stereo image pair is sufficient for satisfactory depth perception, has been used to further reduce the bit rates. Thus, one of the streams is independently coded along the lines of the MPEG standards, while the other stream is estimated at a lower resolution from this stream. A multiresolution framework has been adopted to facilitate such an estimation of motion and disparity vectors at different resolutions. Experimental results on typical sequences indicate that the additional stream can be compressed to about one-fifth of a highly compressed independently coded stream, without any significant loss in depth perception or perceived image quality. >

35 citations

##### 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. >

19,033 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.

8,350 citations

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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.

6,550 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.

617 citations

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