# 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 …

##### Citations

More filters

••

15 Mar 1999TL;DR: A wavelet based stereo image pair coding algorithm is proposed that is efficient to achieve stereo image compression and improves the accuracy of estimation of wavelet images produced by the disparity compensation technique.

Abstract: Stereo image pair coding is an important issue in stereo data compression. A wavelet based stereo image pair coding algorithm is proposed in this paper. The wavelet transform is used to decompose the image into an approximation image and three edge images. In the wavelet domain, a disparity estimation technique is developed to estimate the disparity field using both approximation image and edge images. To improve the accuracy of estimation of wavelet images produced by the disparity compensation technique, a novel wavelet based subspace projection technique (SPT) is developed. In the SPT, the block dependent subspaces are constructed using block varying basis vectors that are derived from the disparity compensated wavelet images. Experimental results show that the proposed algorithm is efficient to achieve stereo image compression.

25 citations

••

17 Apr 1995TL;DR: In this article, a disparity-based segmentation approach is proposed to achieve an efficient partition of the image into regions of more or less fixed disparity, in order to minimize the edge artifacts after disparity compensation.

Abstract: Stereoscopic image sequence transmission over existing monocular digital transmission channels, without seriously affecting the quality of one of the image streams, requires a very low bit-rate coding of the additional stream. Fixed block-size based disparity estimation schemes cannot achieve such low bit-rates without causing severe edge artifacts. Also, textureless regions lead to spurious matches which hampers the efficient coding of block disparities. In this paper, we propose a novel disparitybased segmentation approach, to achieve an efficient partition of the image into regions of more or less fixed disparity. The partitions are edge based, in order to minimize the edge artifacts after disparity compensation. The scheme leads to disparity discontinuity preserving, yet smoother and more accurate disparity fields than fixed block-size based schemes. The smoothness and the reduced number of block disparities lead to efficient coding of one image of a stereo pair given the other. The segmentation is achieved by performing a quadtree decomposition, with the disparity compensated error as the splitting criterion. The multiresolutional recursive decomposition offers a computationally efficient and non-iterative means of improving the disparity estimates while preserving the disparity discontinuities. The segmented regions can be tracked temporally to achieve very high compression ratios on a stereoscopic image stream.

23 citations

•

07 Jul 2011TL;DR: In this paper, a time efficient stereo matching method which is applicable at an algorithm level is presented, which is compatible with and thus can be employed to any types of stereo matching implementation.

Abstract: Unlike previous works with emphasis on hardware level optimization for the processing time reduction in stereo matching, the present invention provides a time efficient stereo matching method which is applicable at an algorithm level, which is compatible with and thus can be employed to any types of stereo matching implementation.

21 citations

••

10 Oct 2005TL;DR: In this paper, an improved interpolated motion and disparity estimation (EIMDE) method was proposed to encode the frames of the right image sequence by exploiting both the temporal redundancy of the same sequence and the disparity redundancy with the left image sequence.

Abstract: A new optimised technique for coding stereoscopic image sequences is presented and compared with already known methods. The proposed technique, called enhanced interpolated motion and disparity estimation (EIMDE), is based on the joint method, which encodes the frames of the right image sequence by exploiting both the temporal redundancy of the same sequence and the disparity redundancy with the left image sequence. In the proposed method, a variable block size scheme has been employed for motion and disparity estimation. The block size is controlled by quad-tree decomposition of the processed frame based on a rate-distortion splitting criterion. For the prediction of a macroblock, optimised motion and disparity vectors are jointly estimated and the participating proportion of each similarity is suitably searched. In this way, the energy of the resulted residual frame is minimised and the whole framework is optimised. Finally, the residual frame is decomposed by a discrete wavelet transform and is further compressed by morphological encoding the resulting coefficients. The proposed coder has been experimentally evaluated on real image sequences, where it produced good performance over other known methods.

20 citations

••

TL;DR: A two-dimensional (2-D) least squares (LS)-based filtering scheme for high fidelity stereo image compression applications is introduced in this correspondence and the results were benchmarked against those of the block-matching method.

Abstract: A two-dimensional (2-D) least squares (LS)-based filtering scheme for high fidelity stereo image compression applications is introduced in this correspondence This method removes the effects of mismatching in a stereo image pair by applying the left image as the reference input to a 2-D transversal filter while the right image is used as the desired output The weights of the filter are computed using a block-based LS method A reduced order filtering scheme is also introduced to find the optimum number of filter coefficients The principal coefficients and the disparity vectors are used together with left image to reconstruct the right image at the receiver, The proposed schemes are examined on a real stereo image pair for 3D-TV applications and the results were benchmarked against those of the block-matching method

16 citations

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

...Block-matching method is used for both disparity estimation [2], [3] as well as motion estimation [10], [11] due to its simplicity and low encoding overhead requirements....

[...]

##### References

More filters

••

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

••

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....

[...]

••

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

••

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