Sparse/DCT (S/DCT) Two-Layered Representation of Prediction Residuals for Video Coding

Kang, Je-Won; Gabbouj, Moncef; Kuo, C.-C. Jay

doi:10.1109/tip.2013.2256917

Cited by 28 publications

(8 citation statements)

References 27 publications

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“…where F F F k (T T T ) = vec −1 [q q q k (T T T )] where K K K K is the commutation matrix. We can thus state (20) in matrix form as…”

Section: Appendixmentioning

confidence: 99%

An Algorithm for Learning Orthonormal Matrix Codebooks for Adaptive Transform Coding

Boragolla¹,

Yahampath²

2023

Preprint

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<p>This paper proposes a novel data-driven approach to designing orthonormal transform matrix codebooks for adaptive transform coding of any non-stationary vector processes which can be considered locally stationary. Our algorithm, which belongs to the class of block-coordinate descent algorithms, relies on simple probability models such as Gaussian or Laplacian for transform coefficients to directly minimize with respect to the orthonormal transform matrix the mean square error (MSE) of scalar quantization and entropy coding of transform coefficients. A difficulty commonly encountered in such minimization problems is imposing the orthonormality constraint on the matrix solution. We get around this difficulty by mapping the constrained problem in Euclidean space to an unconstrained problem on the Stiefel manifold and leveraging known algorithms for unconstrained optimization on manifolds. While the basic design algorithm directly applies to non-separable transforms, an extension to separable transforms is also proposed. We present experimental results showing that adaptive coding with our transform codebook designs outperform the commonly used discrete cosine transform (DCT) in many regions of natural images and motion-compensated prediction error images of video frames. According to these results, the proposed codebook designs slightly outperform those found by a common alternative approach, the sparsity-based optimization. </p>

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“…where F F F k (T T T ) = vec −1 [q q q k (T T T )] where K K K K is the commutation matrix. We can thus state (20) in matrix form as…”

Section: Appendixmentioning

confidence: 99%

An Algorithm for Learning Orthonormal Matrix Codebooks for Adaptive Transform Coding

Boragolla¹,

Yahampath²

2023

Preprint

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show abstract

“…The scheme is able to preserve most of the information and avoid absolutely blindly estimating the highfrequency components. Although this is not the first time to combine CS with DCT in the field of image processing, the methodology of our proposed method is essentially different from that of those existing methods [32]- [34] based on DCT and CS. We now summarize our novel contributions in comparison with existing methods:…”

Section: Introductionmentioning

confidence: 97%

“…In [33], image was decomposed in texture and piecewise smooth content. In order to decompose image, the work [34] used dictionary training to find the best overcomplete dictionary and the residual in the DCT domain. While we preserve the low-frequency DCT components and compressed sample the high-frequency DCT components in the spatial domain by using CS.…”

Section: Introductionmentioning

confidence: 99%

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Image Arbitrary-Ratio Down- and Up-Sampling Scheme Exploiting DCT Low Frequency Components and Sparsity in High Frequency Components

Zhang

Chen

Shi

et al. 2016

IEICE Trans. Inf. & Syst.

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The development of image acquisition technology and display technology provide the base for popularization of high-resolution images. On the other hand, the available bandwidth is not always enough to data stream such high-resolution images. Down-and up-sampling, which decreases the data volume of images and increases back to high-resolution images, is a solution for the transmission of high-resolution images. In this paper, motivated by the observation that the high-frequency DCT components are sparse in the spatial domain, we propose a scheme combined with Discrete Cosine Transform (DCT) and Compressed Sensing (CS) to achieve arbitrary-ratio down-sampling. Our proposed scheme makes use of two properties: First, the energy of a image concentrates on the lowfrequency DCT components. Second, the high-frequency DCT components are sparse in the spatial domain. The scheme is able to preserve the most information and avoid absolutely blindly estimating the highfrequency components. Experimental results show that the proposed downand up-sampling scheme produces better performance compared with some state-of-the-art schemes in terms of peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM) and processing time.

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“…[29] designed quadtree-based and learning-based interlayer prediction mode to support single-loop and multi-loop solutions for spatial scalability. Furthermore, Kang et al [30] considered a cascaded two-layer representation of prediction residuals in HEVC, where sparse representation is adopted to capture regular patterns of residuals derived by DCT in a sequential manner. To optimize inter-layer prediction, Han et al [31] formulated an estimation-theoretic framework that optimized the conditional distribution for relevant information from base and enhancement layers.…”

Section: Introductionmentioning

confidence: 99%

Progressive Dictionary Learning With Hierarchical Predictive Structure for Low Bit-Rate Scalable Video Coding

Dai

Shen

Xiong

et al. 2017

IEEE Trans. on Image Process.

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Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between the neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a closed-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the state-of-the-art scalable extension of H.264/AVC and latest High Efficiency Video Coding (HEVC), standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest scalable extension of HEVC and HEVC simulcast over extensive test sequences with various resolutions.

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Sparse/DCT (S/DCT) Two-Layered Representation of Prediction Residuals for Video Coding

Cited by 28 publications

References 27 publications

An Algorithm for Learning Orthonormal Matrix Codebooks for Adaptive Transform Coding

An Algorithm for Learning Orthonormal Matrix Codebooks for Adaptive Transform Coding

Image Arbitrary-Ratio Down- and Up-Sampling Scheme Exploiting DCT Low Frequency Components and Sparsity in High Frequency Components

Progressive Dictionary Learning With Hierarchical Predictive Structure for Low Bit-Rate Scalable Video Coding

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