Stereo Image Coding Research Articles

Sparse optimization of non separable vector lifting scheme for stereo image coding

One of the potential 3D imaging techniques relies on the use of stereoscopic systems. The great interest in these systems has resulted in huge amount of data which needs to be compressed for storage and transmission purposes. In this context, vector lifting scheme has been found to be an efficient approach for stereo image coding. For instance, the coding performance depends on the design of the involved lifting operators referred to as prediction and update filters. For this reason, while a non separable vector lifting structure is retained, we investigate different techniques for optimizing sparse criteria to design the filters used with both views. More precisely, an independent full optimization algorithm as well as a joint algorithm will be developed and studied. Simulations performed on different stereo images demonstrate the effectiveness of the proposed sparse optimization algorithms in terms of quality of reconstruction and bitrate saving.

Recurrent pattern matching based stereo image coding using linear predictors

The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based generic image encoding solution which has been investigated earlier for the compression of stereo images with successful results. While first MMP-based proposals for stereo image coding employed dictionary-based techniques for disparity compensation, posterior developments have demonstrated the advantage of using predictive methods. In this paper, we focus on recent investigations on the use of predictive methods in the MMP algorithm and propose a new prediction framework for efficient stereo image coding. This framework comprises an advanced intra directional prediction model and a new linear predictive scheme for efficient disparity compensation. The linear prediction model is the main novelty of this work, combining adaptive linear models estimated by least-squares algorithm with fixed linear models provided by the block-matching algorithm. The performance of the proposed intra prediction and disparity compensation methods when applied in an MMP encoder has been evaluated experimentally. Comparisons with the current stereo image coding standards showed that the proposed MMP algorithm significantly outperforms the Stereo High Profile of H.264/AVC standard. In addition, it presents a competitive performance relative to the MV-HEVC standard. These results also suggest that current stereo image coding standards may benefit from the proposed linear prediction scheme for disparity compensation, as an extension to the omnipresent block-matching solution.

A Novel Disparity Estimation Method Based on Multi-Block and Adaptive Window

Disparity estimation is a basic analytic method for images and video and is highly used in fields like stereo image coding, video retrieval and stereo image segmentation. This paper proposes a new disparity estimation method. In this paper, the superiority of the multi-block matching is analyzed. The new method based on the idea of block matching constructs a multi-block matching algorithm, thereby improving matching accuracy especially in the disparity discontinuous regions. A adaptive window method based on edge detect is proposed to select the best window.The experimental results based on the Middlebury stereo datasets demonstrate that our method can achieve a better performance.

Non-separable lifting scheme with adaptive update step for still and stereo image coding

Many existing works related to lossy-to-lossless multiresolution image compression are based on the lifting concept. It is worth noting that a separable lifting scheme may not appear very efficient to cope with the 2D characteristics of edges which are neither horizontal nor vertical. In this paper, we propose to use 2D non-separable lifting schemes that still enable progressive reconstruction and exact decoding of images. Their relevant advantage is to yield a tractable optimization of all the involved decomposition operators. More precisely, we design the prediction operators by minimizing the variance of the detail coefficients. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artifacts. A theoretical analysis of the proposed method is conducted in terms of the adaptation criterion considered in the optimization of the update filter. Simulations carried out on still images and residual ones generated from stereo pairs show the benefits which can be drawn from the proposed optimization of the lifting operators.

A family of wavelet-based stereo image coders

We propose novel algorithms for stereoscopic image coding based on the hierarchical decomposition of stereo information. The proposed schemes, based on the wavelet transform and zerotree quantization, are endowed with excellent progressive transmission capability and retain the option for perfect reconstruction of the original image pair. Experimental evaluation shows that the resulting methods produce superior results when compared with other algorithms for stereo image coding. This is achieved without introducing blocking artifacts and with the valuable additional convenience of the production of embedded bitstreams.

Joint rate-distortion optimization of discrete cosine transform coefficient quantization and disparity field estimation for stereo image coding

Disparity-compensated residual coding (DCRC) is the most popular approach to stereo image coding because of its simplicity and good performance. Researches on DCRC have investigated two main topics: quantization of discrete cosine transform (DCT) coefficients and disparity field estimation and compensation. It is shown that quantization problems can be optimized in a rate-distortion (RD) sense under the fixed-disparity constraint and that the RD-optimized disparity field can be obtained under the fixed-quantizer constraint. This paper proposes a technique that jointly optimizes DCT coefficient quantization and dispar- ity field estimation using the three-dimensional Viterbi algorithm with re- spect to the RD theory under three constraints: epipolar constraint, inter- block disparity constraint, and intrablock disparity constraint. The proposed algorithm reduces not only the bit rate of the disparity map but also that of DCT coefficients, leading to performance improvement. Also, it overcomes shortcomings of conventional methods and is well applied to the narrow-angle stereo camera systems. The effectiveness of the proposed algorithm is shown via computer simulations. © 2002 Society of

Edge-preserving disparity estimation and disparity-compensated intermediate view reconstruction for stereo images

A constrained disparity estimation method is proposed which uses a directional regularization technique to efficiently preserve edges for stereo image coding. The proposed method smoothes disparity vectors in smooth regions and preserves edges in object boundaries well, without creating an oversmoothing problem. The differential pulse code modulation (DPCM) technique for disparity map coding is used prior to entropy coding, in order to improve the overall coding efficiency. The proposed disparity estimation method can also be applied to intermediate view reconstruction. Intermediate views between a left image and a right image provide reality and natural motion parallax to multiviewers. Intermediate views are synthesized by appropriately exploiting an interpolation or an extrapolation technique according to the characteristics of each region after identifying the regions as occluded regions, normal regions, and regions having ambiguous disparities.

Overlapped block disparity compensation with adaptive windows for stereo image coding

We propose a modified overlapped block-matching (OBM) scheme for stereo image coding. OBM has been used in video coding but, to the best of our knowledge, it has not been applied to stereo image coding to date. In video coding, OBM has proven useful in reducing blocking artifacts (since multiple vectors can be used for each block), while also maintaining most of the advantages of fixed-size block matching. There are two main novelties in this work. First, we show that OBM techniques can be successfully applied to stereo image coding. Second, we take advantage of the smoothness properties typically found in disparity fields to further improve the performance of OBM in this particular application. Specifically, we note that practical OBM approaches use noniterative estimation techniques, which produce lower quality estimates than iterative methods. By introducing smoothness constraints into the noniterative DV computation, we improve the quality of the estimated disparity as compared to standard noniterative OBM approaches. In addition, we propose a disparity estimation/compensation approach using adaptive windows with variable shapes, which results in a reduction in complexity. We provide experimental results that show that our proposed hybrid OBM scheme achieves a PSNR gain (about 1.5-2 dB) as compared to a simple block-based scheme, with some slight PSNR gains (about 0.2-0.5 dB) in a reduced complexity, as compared to an approach based on standard OBM with half-pixel accuracy.

Optimal blockwise dependent quantization for stereo image coding

Research in coding of stereo images has focused mostly on the issue of disparity estimation to exploit the redundancy between the two images in a stereo pair, with less attention being devoted to the equally important problem of allocating bits between the two images. This bit allocation problem is complicated by the dependencies arising from using a prediction based on the quantized reference images. We address the problem of blockwise bit allocation for coding of stereo images and show how, given the special characteristics of the disparity field, one can achieve an optimal solution with reasonable complexity, whereas in similar problems in motion compensated video only approximate solutions are feasible. We present algorithms based on dynamic programming that provide the optimal blockwise bit allocation. Our experiments based on a modified JPEG coder show that the proposed scheme achieves higher mean peak signal-to-noise ratio over the two frames (0.2-0.5 dB improvements) as compared with blockwise independent quantization. We also propose a fast algorithm that provides most of the gain at a fraction of the complexity.

Stereo image coding: a projection approach

Recently, 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.

Compression of stereo images using subsampling and transform coding

The proposed approach to stereo image coding takes advantage of the singleness of vision property of the human visual system. A stereo image pair, in which one of the images is low-pass filtered and subsampled, is perceived stereoscopically as a sharp 3-D image. The depth information is perceived due to the stereopsis effect, and the sharpness is maintained due to the details in the nonfiltered image. Low-pass filtering, subsampling, and discrete cosine transform image coding are used for the compression of the stereo images. A methodology for the evaluation of the compression effects on the 3-D perception of compressed stereo images is presented. It is based on measurements of response time and accuracy of human subjects performing simple 3-D perception tasks.