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
01 Jan 2000
8 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....
[...]
11 Jun 1997
7 citations
01 Jan 1997
TL;DR: In this paper, two predictive coding algorithms for 3-0 stereo image compression are proposed, which make use of inter-frame and intra-frame correlation present in stereo image pairs.
Abstract: In this paper, we propose two predictive coding algorithms for 3-0 stereo image compression which makes use of inter-frame and intra-frame correlation present in stereo image pairs. The algorithm is applied to right frames of stereo image pairs on top of DCTquantization based compression technology to achieve extra data compression. The technology can also be applied in addition to any other compression technology such as wavelets and fractals etc.
7 citations
12 Dec 2008
TL;DR: The major feature of this scheme is that not only its computational load is decreased drastically for calculating the disparity vectors compared to true disparity estimation (DE) and disparity compensation (DC)-based algorithm, but also, its reconstructed image quality is better than true DE/DC-based algorithm.
Abstract: This paper presents an efficient disparity estimation-based scheme for stereo image coding This scheme makes use of a pseudo-disparity image instead of a true (estimated) disparity map to reconstruct the target image Moreover, a block-partition approach is utilized to improve the reconstructed image quality of object edges The major feature of this scheme is that not only its computational load is decreased drastically for calculating the disparity vectors compared to true disparity estimation (DE) and disparity compensation (DC)-based algorithm, but also, its reconstructed image quality is better than true DE/DC-based algorithm Furthermore, in the proposed scheme, a reference image is compressed by lossy JPEG2000, while a pseudo disparity image is encoded by a lossless way for good reconstruction of the target image The simulation results show that the PSNR is better than true DE/DC-based algorithm at the identical bit rate
7 citations
Cites methods from "Multiresolution based hierarchical ..."
...This is achieved by computing a disparity field between stereo image pair [2-6]....
[...]
TL;DR: Predictive coding is applied to one of the two frames associated with each stereo image pair on top of DCT-quantization based compression technology to achieve extra data compression.
Abstract: We propose two predictive coding algorithms for 3-D stereo image compression which make use of inter-frame and intra-frame correlation present in stereo image pairs. The predictive coding is applied to one of the two frames associated with each stereo image pair on top of DCT-quantization based compression technology to achieve extra data compression. The technology can also be applied in addition to any other compression technology such as, wavelets based, fractal based, etc. This novel coding technique eliminates the necessity of using overhead bits in reconstructing the predicted image.
6 citations
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
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