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173 results on '"Serra-Sagrista, Joan"'

2. Rice-Marlin Codes: Tiny and Efficient Variable-to-Fixed Codes

Author

Martinez, Manuel and Serra-Sagristà, Joan

Subjects
Computer Science - Information Theory
Abstract

Marlin is a Variable-to-Fixed (VF) codec optimized for high decoding speed through the use of small sized dictionaries that fit in the L1 cache of most CPUs. While the size of Marlin dictionaries is adequate for decoding, they are still too large to be encoded fast. We address this problem by proposing two techniques to reduce the alphabet size. The first technique is to encode rare symbols in their own segment, and the second is to combine Marlin dictionaries with Rice encoding, hence our name Rice-Marlin for our new codec. Using those techniques, we are able to reduce the size of Marlin dictionaries by a factor of 16, not only enabling faster encoding speed, but also achieving better compression efficiency.

Published
2018

3. Lower Bounds on the Redundancy of Huffman Codes with Known and Unknown Probabilities

Author

Blanes, Ian, Hernández-Cabronero, Miguel, Serra-Sagristà, Joan, and Marcellin, Michael W.

Subjects
Computer Science - Information Theory
Abstract

In this paper we provide a method to obtain tight lower bounds on the minimum redundancy achievable by a Huffman code when the probability distribution underlying an alphabet is only partially known. In particular, we address the case where the occurrence probabilities are unknown for some of the symbols in an alphabet. Bounds can be obtained for alphabets of a given size, for alphabets of up to a given size, and for alphabets of arbitrary size. The method operates on a Computer Algebra System, yielding closed-form numbers for all results. Finally, we show the potential of the proposed method to shed some light on the structure of the minimum redundancy achievable by the Huffman code.

Published
2018
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6. Point cloud data compression

Author

Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Vellido Alcacena, Alfredo, Serra Sagrista, Joan, Gururajan, Ashwin Kumar, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Vellido Alcacena, Alfredo, Serra Sagrista, Joan, and Gururajan, Ashwin Kumar

Abstract

The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a substantial research effort dedicated to developing efficient compression algorithms for point cloud data. This Master's thesis aims to investigate the current state-of-the-art lossless point cloud geometry compression techniques, explore some of these techniques in more detail and then propose improvements and/or extensions to enhance them and provide directions for future work on this topic.

Published
2023

10. JPEG2000 Coding Techniques Addressed to Images Containing No-Data Regions

Author

González-Conejero, Jorge, Aulí-Llinàs, Francesc, Bartrina-Rapesta, Joan, Serra-Sagristà, Joan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Blanc-Talon, Jacques, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2007
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12. BISK Scheme Applied to Sign Encoding and to Magnitude Refinement

Author

Bras-Amorós, Maria, Guitart-Colom, Pere, González-Conejero, Jorge, Serra-Sagristà, Joan, García-Vílchez, Fernando, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Blanc-Talon, Jacques, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2005
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13. Review of Coding Techniques Applied to Remote Sensing

Author

Serra-Sagrista, Joan, Auli, Francesc, Garcia, Fernando, Gonzalez, Jorge, Guitart, Pere, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Negoita, Mircea Gh., editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2004
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15. Reduced-Complexity Multirate Remote Sensing Data Compression With Neural Networks.

Author

Verdu, Sebastia Mijares i, Chabert, Marie, Oberlin, Thomas, and Serra-Sagrista, Joan

Abstract

One of the main limitations to the adoption of deep learning for image compression is the need to train multiple models to compress at multiple rates. In the case of onboard remote sensing data compression, another limitation is the computational cost of the neural networks. Addressing both limitations, this letter presents a new reduced-complexity architecture for multirate compression of remote sensing images. The proposed architecture enables compressing at a precise user-selected rate while keeping a competitive performance in lossy compression on different sets of remote sensing data. The proposed approach is amenable for onboard deployment. [ABSTRACT FROM AUTHOR]

Published
2023
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24. Point AE-DCGAN: A deep learning model for 3D point cloud lossy geometry compression

Author

Xu, Jiacheng, Fang, Zhijun, Gao, Yongbin, Ma, Siwei, Jin, Yaochu, Zhou, Heng, Wang, Anjie, Bilgin, Ali, Marcellin, Michael W., Serra-Sagrista, Joan, and Storer, James A.

Subjects
Computer science, Point cloud, Codec, Augmented reality, Point (geometry), Data_CODINGANDINFORMATIONTHEORY, Deconvolution, Lossy compression, Autoencoder, Algorithm, Data compression
Abstract

3D point cloud has been widely applied in virtual reality and augmented reality. A complex 3D scene always needs a large number of the point cloud to represent and demands a lot of space to store. Thus, point cloud compression becomes a crucial issue to research. In this paper, we propose a novel lossy geometric compression method of autoencoder based on DCGAN optimization. This method can reconstruct a high-quality point cloud and solves a large area of missing points in the process of compression and decompression. To improve the point cloud codec performance, we propose a multi-scale 3D deconvolution hopping connection structure to obtain a better-quality reconstructed point cloud under low bit rates. Our approach is the first GAN-based point cloud compression algorithm to our knowledge. Compared with state-of-the-art methods on the MVUB dataset, our approach achieves a better rate-distortion performance and visual quality.

Published
2021
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27. JPEG2000 quality scalability without quality layers

Author

Auli-Llinas, Francesc and Serra-Sagrista, Joan

Subjects
Computer graphics -- Standards, Mathematical optimization -- Research, Image processing -- Technology application, Technology application, Business, Computers, Electronics, Electronics and electrical industries
Abstract

Quality scalability is a fundamental feature of JPEG2000, achieved through the use of quality layers that are optimally formed in the encoder by rate-distortion optimization techniques. Two points, related with the practical use of quality layers, may need to be addressed when dealing with JPEG2000 code-streams: 1) the lack of quality scalability of code-streams containing a single or few quality layers and 2) the rate-distortion optimality of windows of interest transmission. Addressing these two points, this paper proposes a mechanism that, without using quality layers, provides competitive quality scalability to code-streams. Its main key-feature is a novel characterization of the code-blocks rate-distortion contribution that does not use distortion measures based on the original image, or related with the encoding process. Evaluations against the common use of quality layers, and against a theoretical optimal coding performance when decoding windows of interest or when decoding the complete image area, suggest that the proposed method achieves close to optimal results. Index Terms--Interactive image transmission, JPEG2000 standard, quality scalability, rate-distortion optimization.

Published
2008

34. Review and Implementation of the Emerging CCSDS Recommended Standard for Multispectral and Hyperspectral Lossless Image Coding

Author

Sanchez, Jose Enrique, Auge, Estanislau, Santalo, Josep, Blanes, Ian, Serra-Sagrista, Joan, and Kiely, Aaron

Subjects
Instrumentation And Photography
Abstract

A new standard for image coding is being developed by the MHDC working group of the CCSDS, targeting onboard compression of multi- and hyper-spectral imagery captured by aircraft and satellites. The proposed standard is based on the "Fast Lossless" adaptive linear predictive compressor, and is adapted to better overcome issues of onboard scenarios. In this paper, we present a review of the state of the art in this field, and provide an experimental comparison of the coding performance of the emerging standard in relation to other state-of-the-art coding techniques. Our own independent implementation of the MHDC Recommended Standard, as well as of some of the other techniques, has been used to provide extensive results over the vast corpus of test images from the CCSDS-MHDC.

Published
2011

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