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About 9,359 papers for “Transform coding”

Transform Coding for PUFs

Onur Günlü, Onurcan İşcan · mediaTUM (Technical University of Munich) · 2015

Cited by 0Open access

Dithered GMD Transform Coding

Ching-Chih Weng, P. P. Vaidyanathan, Han-I Su · IEEE Signal Processing Letters · 2010

The geometric mean decomposition (GMD) transform coder (TC) was recently introduced and was shown to achieve the optimal coding gain without bit loading under the high bit rate assumption. However, the performance of the GMD transform coder is degraded in the low rate case. There are mainly two reasons for this degradation. First, the high bit rate quantizer model becomes invalid. Second, the quantization error is no longer negligible in the prediction process when the bit rate is low. In this letter, we introduce dithered quantization to tackle the first difficulty, and then redesign the precoders and predictors in the GMD transform coders to tackle the second. We propose two dithered GMD transform coders: the GMD subtractive dithered transform coder (GMD-SD) where the decoder has access to the dither information and the GMD nonsubtractive dithered transform coder (GMD-NSD) where the decoder has no knowledge about the dither. Under the uniform bit loading scheme in scalar quantizers, it is shown that the proposed dithered GMD transform coders perform significantly better than the original GMD coder in the low rate case.

Cited by 3

Toward Textual Transform Coding

Tsachy Weissman · arXiv (Cornell University) · 2023

Inspired by recent work on compression with and for young humans, the success of transform-based approaches to information processing, and the rise of powerful language-based AI, we propose \emph{textual transform coding}. It shares some of its key properties with traditional transform-based coding underlying much of our current multimedia compression technologies. It can form the basis for compression at bit rates until recently considered uselessly low, and for boosting human satisfaction from reconstructions at more traditional bit rates.

Cited by 0Open access

Optimally adaptive transform coding

R.D. Dony, S. Haykin · IEEE Transactions on Image Processing · 1995

The optimal linear block transform for coding images is well known to be the Karhunen-Loeve transformation (KLT). However, the assumption of stationarity in the optimality condition is far from valid for images. Images are composed of regions whose local statistics may vary widely across an image. While the use of adaptation can result in improved performance, there has been little investigation into the optimality of the criterion upon which the adaptation is based. In this paper we propose a new transform coding method in which the adaptation is optimal. The system is modular, consisting of a number of modules corresponding to different classes of the input data. Each module consists of a linear transformation, whose bases are calculated during an initial training period. The appropriate class for a given input vector is determined by the subspace classifier. The performance of the resulting adaptive system is shown to be superior to that of the optimal nonadaptive linear transformation. This method can also be used as a segmentor. The segmentation it performs is independent of variations in illumination. In addition, the resulting class representations are analogous to the arrangement of the directionally sensitive columns in the visual cortex.

Cited by 92

Reduction of coding artifacts in transform image coding

Robert Louis Stevenson · IEEE International Conference on Acoustics Speech and Signal Processing · 1993

The problem of image decompression is cast as an ill-posed inverse problem, and a stochastic regularization technique is used to form a well-posed reconstruction algorithm. A statistical model for the image which incorporates the convex Huber minimax function is proposed. The use of the Huber minimax function rho T(.) helps to maintain the discontinuities from the original image which produces high-resolution edge boundaries. Since rho T(.) is convex, the resulting multidimensional minimization problem is a constrained convex optimization problem. The maximum a posteriori (MAP) estimation technique that is proposed results in the constrained optimization of a convex functional. The proposed image decompression algorithm produces reconstructed images which greatly reduced the noticeable artifacts which exist using standard techniques.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 90

NVTC: Nonlinear Vector Transform Coding

Runsen Feng, Zongyu Guo, Weiping Li et al. · 2023

In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (RD) performance [33]. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of VQ due to its exponentially increased complexity. In this paper, we first investigate on some toy sources, demonstrating that even if modern neural networks considerably enhance the compression performance of SQ with nonlinear transform, there is still an insurmountable chasm between SQ and VQ. Therefore, revolving around VQ, we propose a novel framework for neural image compression named Nonlinear Vector Transform Coding (NVTC). NVTC solves the critical complexity issue of VQ through (1) a multi-stage quantization strategy and (2) nonlinear vector transforms. In addition, we apply entropy-constrained VQ in latent space to adaptively determine the quantization boundaries for joint rate-distortion optimization, which improves the performance both theoretically and experimentally. Compared to previous NTC approaches, NVTC demonstrates superior rate-distortion performance, faster decoding speed, and smaller model size. Our code is available at https://github.com/USTC-IMCL/NVTC.

Cited by 16

Optimum transform coding of imagery

Glen P. Abousleman · 1999

A system is presented for transform coding of imagery. Specifically, the system uses the 2-D discrete cosine transform (DCT) in conjunction with adaptive classification, entropy-constrained trellis-coded quantization, optimal rate allocation, and adaptive arithmetic encoding. Adaptive classification, side rate reduction, and rate allocation strategies are discussed. Entropy-constrained codebooks are designed using a modified version of the generalized Lloyd algorithm. This entropy-constrained DCT-based system is shown to achieve outstanding coding performance as compared to other DCT-based systems.

Cited by 0

H.264 Transform and Coding

Iain E. G. Richardson · 2010

This chapter contains sections titled: Introduction Transform and quantization Block scan orders Coding Summary References

Cited by 0

An Adaptive Transform Coding Algorithm

Andrew G. Tescher, R.V. Cox · 1976

Abstract : An adaptive transform coding algorithm based on a recursive procedure in the transform domain has been developed. Both the quantization parameters and bit assignment are dynamically determined and thus are closely matched to the actual image structure. Overhead requirements are minimal.

Cited by 22

Transform selection for video coding

NamUk Kim, Yung-Lyul Lee · 2020

- In video coding algorithm, a transform is one of the most important video compression tools. Transform converts residual signals into frequency domain data to get decorrelated signals. Decorrelated data using transforms is used for coding efficiency improvement in video compression. In this paper, an efficient transform selection method in term of video compression is described by using transform block size and intra coding mode on the top of the HEVC standard.

Cited by 0

Image coding using wavelet transform

Marc Antonini, Michel Barlaud, Pierre-Philippe Mathieu et al. · IEEE Transactions on Image Processing · 1992

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.

Cited by 3,515

Image transform for video coding

Flierl, Markus, Girod, Bernd, Vandergheynst, Pierre · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2012

A method is disclosed for decomposing a set of even and odd pictures into low-band and high-band pictures respectively in a image decomposing unit, in which the even picture is used by at least two prediction motion compensators on which the output signal of each prediction motion compensator is scaled according to the number of prediction motion compensators. The method includes calculating the high-band picture by subtracting from the odd picture the scaled motion-compensated signals and using the high-band picture in the at least two update motion compensators, the output signal of each update motion compensator being scaled according to the number of update motion compensators. Finally, the low-band picture is calculated by adding the scaled update motion-compensated signals to the even picture.

Cited by 0Open access

Graph transform coding For AV2

Segui Ugalde, Aleix · QRU Quaderns de Recerca en Urbanisme · 2023

Es presenta una exploració de les Transformades en Grafs amb l'objectiu de millorar els algorismes de codificació en un còdec de video actual, AV2. Definim una versió paramètrica de transformada, basada en Grafs Lineals, i trobem una expressió per als paràmetres òptims en base a un model teòric de la senyal. A banda, explorem el concepte de transformades irregulars, que generalitzen la formulació i ens permeten regular la importància de cada pixel individualment. Finalment, presentem una implementació eficient de les Transformades en Grafs al software de referència d'AV2, aconseguint un guany de compressió d'un 0.16%.

Cited by 0Open access

Theoretical foundations of transform coding

Vivek K Goyal · IEEE Signal Processing Magazine · 2001

Discusses various aspects of transform coding, including: source coding, constrained source coding, the standard theoretical model for transform coding, entropy codes, Huffman codes, quantizers, uniform quantization, bit allocation, optimal transforms, transforms visualization, partition cell shapes, autoregressive sources, transform optimization, synthesis transform optimization, orthogonality and independence, and departures form the standard model.

Cited by 394