Title:A Water-filling Algorithm Maximizing the Volume of Submatrices Above the Rank

Authors: C. Petit, A. Roumy, T. Maugey

Abstract: In this paper, we propose an algorithm to extract, from a given rectangular matrix, a submatrix with maximum volume, whose number of extracted columns is greater than the initial matrix rank. This problem arises in compression and summarization of databases, recommender systems, learning, numerical analysis or applied linear algebra. We use a continuous relaxation of the maximum volume matrix extraction problem, which admits a simple and closed form solution: the nonzero singular values of the extracted matrix must be equal. The proposed algorithm extracts matrices with singular values, which are close to be equal. It is inspired by a water-filling technique, traditionally dedicated to equalization strategies in communication channels. Simulations show that the proposed algorithm performs better than sampling methods based on determinantal point processes (DPPs) and achieves similar performance as the best known algorithm, but with a lower complexity.

Title: Learning on entropy coded data using CNN

Authors: R. Piau, T. Maugey, A. Roumy

Abstract: We propose an empirical study to see whether learning with convolutional neural networks (CNNs) on entropy coded data is possible. First, we define spatial and semantic closeness, two key properties that we experimentally show to be necessary to guarantee the efficiency of the convolution. Then, we show that these properties are not satisfied by the data processed by an entropy coder. Despite this, our experimental results show that learning in such difficult conditions is still possible, and that the performance are far from a random guess. These results have been obtained thanks to the construction of CNN architectures designed for 1D data (one based on VGG, the other on ResNet). Finally we propose some experiments that explains why CNN are still performing reasonably well on entropy coded data.

Title: Machine learning for multimedia communications

Authors: Nikolaos Thomos, Thomas Maugey, Laura Toni

Abstract: Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been reached all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user’s perception modeling have widely benefited from the recent
learning-oriented developments. However, learning-based algorithms often imply drastic changes on the way data is represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all over the transmission chain, and we discuss their potential impact and the research challenges that they raise.

Title: Large Database Compression Based on Perceived Information

Authors: Thomas Maugey and Laura Toni

Abstract: Lossy compression algorithms trade bits for quality, aiming at reducing as much as possible the bitrate needed to represent the original source (or set of sources), while preserving the source quality. In this letter, we propose a novel paradigm of compression algorithms, aimed at minimizing the information loss perceived by the final user instead of the actual source quality loss, under compression rate constraints.
As main contributions, we first introduce the concept of perceived information (PI), which reflects the information perceived by a given user experiencing a data collection, and which is evaluated as the volume spanned by the sources features in a personalized latent space.
We then formalize the rate-PI optimization problem and propose an algorithm to solve this compression problem. Finally, we validate our algorithm against benchmark solutions with simulation results, showing the gain in taking into account users’ preferences while also maximizing the perceived information in the feature domain.

Title: Excess rate for model selection in interactive compression using Belief-propagation decoding

Authors: Navid Mahmoudian-Bidgoli, Thomas Maugey, Aline Roumy

Abstract: Interactive compression refers to the problem of compressing data while sending only the part requested by the user. In this context, the challenge is to perform the extraction in the compressed domain directly. Theoretical results exist, but they assume that the true distribution is known. In practical scenarios instead, the distribution must be estimated. In this paper, we first formulate the model selection problem for interactive compression and show that it requires to estimate the excess rate incurred by mismatched decoding. Then, we propose a new expression to evaluate the excess rate of mismatched decoding in a practical case of interest: when the decoder is the belief-propagation algorithm. We also propose a novel experimental setup to validate this closed-form formula. We show a good match for practical interactive compression schemes based on fixed-length Low-Density Parity-Check (LDPC) codes. This new formula is of great importance to perform model and rate selection.

Our paper has been one of the focuses of attention of the last issue of the IEEE MMTC Review-letter.

The letter is available here