nils-herrmann/clean_scirepeval huggingface dataset:
dsp Forum - Future of Video Coding and Transmission

T his discussion focuses on aspects of video coding and transmission that are relevant for next-generation services. The moderators and the forum members bring their expert insights into issues such as video coding performance, architectures (block based, scalable, distributed), robustness, multi-view video coding, and visual quality. The invited forum members are: associate editor of IEEE Signal Processing Magazine). Our readers may agree or disagree with the ideas discussed here. In either case, we invite you to share your comments with us by e-mailing avetro@merl.com and barni@dii.unisi.it.  Is video coding dead? Some feel that, with the higher coding efficiency of the H.264/MPEG-4 Advanced Video Coding (AVC) standard (2x as compared to MPEG-2), perhaps there is not much more to do. I must admit that I have heard this " compression is dead " argument at least four times since I started working in image and video coding in 1976.  There could be different reasons to potentially conclude that no further research in video coding is necessary. From an academic perspective, the end of the story will be reached when we have reached a known bound of compression. For example, this has been the case in channel coding for the additive Gaussian white noise channel (AWGN): after the existence of the Shannon bound was proven, it took about 50 years to actually reach the bound with a practical (i.e., acceptable complexity) scheme. For video signals, we just know that there must be such a bound as well. In other words, for some given distortion we will need to spend some rate typically above zero; but video signals are so complex and so more difficult to be modeled compared to the AGWN channel that we do not know where such a bound actually could be. I personally do not believe we are there yet. Even if we were there, it might be worthwhile to look into the problem of achieving the same bound with less complexity. Currently, the main goal still seems to be walking towards that bound (improving compression) without increasing complexity too much. This goal could still keep the video coding research alive for a couple of years or decades. The same does not apply to industry, where the increasing bandwidth of networks and capacity of storage devices may decrease the need for better compression.
Symmetric line graph transforms for inter predictive video coding

In this paper, we study graph-based transforms for inter predictive video coding. We are motivated by the fact that symmetries in the transform basis are very beneficial for computational efficiency. Based on this, we describe the relationship between bisymmetric matrices and the butterfly structure in fast transform algorithms. We introduce a new class of transforms called Symmetric Line Graph Transforms (SLGTs), of which the discrete cosine transform (DCT) is one particular case. As is well known in the case of the DCT, the bisymmetry property allows us to reduce the number of multiplications by half. We show that, beyond the DCT, useful SLGTs can be defined that have efficient implementation. We propose a specific SLGT that is shown to outperform the DCT for classes of inter residual blocks. While the proposed SLGT approaches the performance of a Karhunen Loeve Transform (KLT) at certain distortion levels, it has lower computation cost.
A graph laplacian matrix learning method for fast implementation of graph fourier transform

In this paper, we propose an efficient graph learning approach for fast graph Fourier transform. We consider a maximum likelihood problem with additional constraints based on a matrix factorization of the graph Laplacian matrix, such that its eigenmatrix is a product of a block diagonal matrix and a butterfly-like matrix. We show that a special case of this problem reduces to a learning problem with constraints enforcing certain graph symmetries. Then, we provide an efficient approximation approach for the general problem without enforcing any symmetry constraint. We use this approach to design a fast nonseparable transform for intra predictive residual blocks in video compression. The resulting transform achieves a better rate-distortion performance than the 2D DCT and the hybrid DCT/ADST transform.
Context adaptive thresholding and entropy coding for very low complexity JPEG transcoding

The ever increasing quantity of user generated photos, nearly all compressed using JPEG, has created a growing storage burden on photo storage and sharing services. This creates the need for compression techniques that take JPEG compressed images as inputs. In this paper we propose two novel very low complexity codecs, ROMP and L-ROMP to recompress JPEG photos, achieving increased coding efficiency by making use of very large entropy coding tables. ROMP is a lossless JPEG recompression codec that achieves 15% average gains over JPEG, while L-ROMP is a lossy codec that can achieve 29% average compression gains over JPEG, by applying coefficient thresholding based on a perceptual criterion to a JPEG image before using the entropy coding of ROMP.
Correlation estimation for distributed source coding under information exchange constraints

Distributed source coding (DSC) depends strongly on accurate knowledge of correlation between sources. Previous works have reported capacity-approaching code constructions when exact knowledge of correlation is available at the encoder. However, in many applications exact correlation information may not be available, and correlation estimation is necessary. While error in estimation is inevitable, the impact of estimation error on compression efficiency has not been sufficiently studied for the DSC problem. In this paper we study correlation estimation subject to complexity constraints, and its impact on coding efficiency in a DSC framework. In particular, we consider the case where estimation entails information exchange between spatially separate sources and thus correlation estimation is subject to rate constraints. We first derive optimal strategies for information exchange that minimize the rate penalty due to inaccurate estimation, under constraints on the number of bits that can be exchanged between sources. Experimental results show that significant gain is possible by optimally exchanging information. We then derive analytical expressions to quantify the rate penalty, and analyze how rate penalty changes with a priori knowledge of correlation. In addition, we present a model-based estimation method which can achieve more accurate estimation results compared to directly inspecting the data.
Entropy- and complexity-constrained classified quantizer design for distributed image classification

In this paper, we address the issue of feature encoding for distributed image classification systems. Such systems often extract a set of features such as color, texture and shape from the raw multimedia data automatically and store them as content descriptors. This content-based metadata supports a wider variety of queries than text-based metadata and thus provides a promising approach for efficient database access and management. When the size of the database becomes large and the number of clients connected to the server increases, the feature data requires a significant amount of storage space and transmission bandwidth. Thus it is useful to devise techniques to compress the features. In this paper, we propose an optimal design of a classified quantizer in a rate-distortion-complexity optimization framework. A decision tree classifier (DTC) is applied to classify the compressed data. We employ the generalized Breiman, Freidman, Olshen, and Stone (G-BFOS) algorithm to design the optimal pre-classifier, which is a pruned sub-tree of the decision tree, and to perform the optimal bit allocation among classes. The optimization is carried out based not only on a rate budget, but also on a coding complexity constraint. We illustrate this framework by showing a texture classification example. Our results show that by using a classified quantizer to encode the features, we are able to improve the percentage of correct classification also leads to a reduction of the number of images transmitted between server and client.
Double frequency test for detecting faults in induction machines

Double frequency tests are used for evaluating stator windings analyzing the temperature. Likewise, signal injection on induction machines is a well-known technique on sensorless motor control fields to find out the rotor's position. Motor current signature analysis (MCSA) is the most widely used method for identifying faults in induction motors. MCSA focuses its efforts on the spectral analysis of stator current. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics respectively. In both cases, the problem of frequency accuracy is very tricky since the sideband harmonic is close to the fundamental harmonic. This paper proposes injecting an additional voltage into the machine under test at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.
Efficient quantization for overcomplete expansions in R/sup N/

The use of quantized redundant expansions is useful in applications where the cost of having oversampling in the representation is much lower than the use of a high-resolution quantization (e.g., oversampled A/D). Most work to date has assumed that simple uniform quantization was used on the redundant expansion and then has dealt with methods to improve the reconstruction. Instead, we consider the design of quantizers for overcomplete expansions. Our goal is to design quantizers such that simple reconstruction algorithms (e.g., linear) provide as good reconstructions as with more complex algorithms. We achieve this goal by designing quantizers with different step sizes for each coefficient of the expansion in such a way as to produce a quantizer with periodic structure.
Video encoder based on lifting transforms on graphs

We propose a complete video encoder based on directional “non-separable” transforms that allow spatial and temporal correlation to be jointly exploited. These lifting-based wavelet transforms are applied on graphs that link pixels in a video sequence based on motion information. In this paper, we first consider a low complexity version of this transform, which can operate on subgraphs without significant loss in performance. We then study coefficient reordering techniques that lead to a more realistic and efficient encoder that the one we presented in our earlier work. Our proposed technique shows encouraging results as compared to a comparable scheme based on the DCT transform.
View synthesis prediction using adaptive depth quantization for 3D video coding

Advanced multiview video systems are able to generate intermediate viewpoints of a 3D scene. In addition to the texture content, corresponding depth is associated with each viewpoint. To improve the coding efficiency of such content, view synthesis prediction can be used to further reduce inter-view redundancy in addition to traditional disparity compensated prediction. However, the predictor generated from the view synthesis process is affected by several factors, including signal properties of the texture, the accuracy of the depth and complexity of the scene, as well as coding errors in both the texture and depth. This paper presents an analysis of view synthesis prediction performance considering these factors. Based on this analysis, an adaptive depth quantization scheme is proposed to improve the depth coding, leading to better view synthesis prediction and overall coding efficiency gains. The proposed scheme is able to achieve an average bit rate savings of 0.9% on the coded and synthesized video with a maximum gain of up to 11.7% on the dependent views in the context of an HEVC-based codec.
Rate control for robust video transmission over burst-error wireless channels

We study the problem of rate control for transmission of video over burst-error wireless channels, i.e., channels such that errors tend to occur in clusters during fading periods. In particular we consider a scenario consisting of packet based transmission with automatic repeat request (ARQ) error control and a back channel. We start by showing how the delay constraints in real time video transmission can be translated into rate constraints at the encoder, where the applicable rate constraints at a given time depend on future channel rates. With the acknowledgments received through the back channel we have an estimate of the current channel state. This information, combined with an a priori model of the channel, allows us to statistically model the future channel rates. Thus the rate constraints at the encoder can be expressed in terms of the expected channel behavior. We can then formalize a rate distortion optimization problem, namely, that of assigning quantizers to each of the video blocks stored in the encoder buffer such that the quality of the received video is maximized. This requires that the rate constraints be included in the optimization, since violating a rate constraint is equivalent to violating a delay constraint and thus results in losing a video block. We formalize two possible approaches. The first one seeks to minimize the distortion for the expected rate constraints given the channel model and current observation. The second approach seeks to allocate bits so as to minimize the expected distortion for the given model. We use both dynamic programming and Lagrangian optimization approaches to solve these problems. Our simulation results demonstrate that both the video distortion at the decoder and packet loss rate can be significantly reduced when incorporating the channel information provided by the feedback channel and the a priori model into the rate control algorithm.
Soft information for source channel coding and decoding

Soft processing is an effective method used in channel decoding/detection for a system comprising concatenated subsystems. We consider the source-channel coder and the channel-source decoder as such a system and demonstrate how the principles of soft processing can be applied to coding and decoding. This is based on the identification of marginal bit distortion measures associated with the source coder and the channel decoder. We demonstrate how these marginal measures may be used to predict end-te-end distortion and design unequal channel protection (static or dynamic).
Electrical monitoring for fault detection in an EMA

The concept of a "More Electric Aircraft" (MEA) leads, among other actions, to the substitution of hydraulic-based actuators for Electro-Mechanical Actuators (EMA). Removal of the engine hydraulic pumps requires fully-operative electrical power actuators and mastery of the flight control architecture. However, unexpected faults and lack of safety hinder the massive use of EMAs in flight control actuators and force the development of new systems and methods for supervision in aircrafts actuators. On-line supervision of the motor drive, by means of electrical signature and harmonics analysis, can detect motor faults in its most preliminary state, thus allowing increasing the EMA reliability. However, for typical non-stationary conditions in EMA operation, classical Fourier transform cannot be applied, and other time-frequency decompositions must be explored. This presents an application of Discrete Wavelet Transform to electrical and mechanical Permanent Magnet Synchronous Motor fault detection. Experimental results are shown and prove that affordable Predictive Testing and Inspection technologies can be used to assure the reliability of the new EMAs.
Edge adaptive graph-based transforms: Comparison of step/ramp edge models for video compression

In this paper, we propose a new edge model for edge adaptive graph-based transforms (EA-GBTs) in video compression. In particular, we consider step and ramp edge models to design graphs used for defining transforms, and compare their performance on coding intra and inter predicted residual blocks. In order to reduce the signaling overhead of block-adaptive coding, a new edge coding method is introduced for the ramp model. Our experimental results show that the proposed methods outperform classical DCT-based encoding and that ramp edge models provide better performance than step edge models for intra predicted residuals.
Wavelet Footprints and Sparse Bayesian Learning for DNA Copy Number Change Analysis

Alterations in the number of DNA copies are very common in tumor cells and may have a very important role in cancer development and progression. New array platforms provide means to analyze the copy number by comparing the hybridization intensities of thousands of DNA sections along the genome. However, detecting and locating the copy number changes from this data is a very challenging task due to the large amount of biological processes that affect hybridization and cannot be controlled. This paper proposes a new technique that exploits the key characteristic that the DNA copy number is piecewise-constant along the genome. First, wavelet footprints are used to obtain a basis for representing the DNA copy number that is maximally sparse in the number of copy number change points. Second, sparse Bayesian learning is applied to infer the copy number changes from noisy array probe intensities. Results demonstrate that sparse Bayesian learning has better performance than matching pursuits methods for this high coherence dictionary. Finally, our results are also shown to be very competitive in performance as compared to state-of-the-art methods for copy number detection.
Graph learning with Laplacian constraints: Modeling attractive Gaussian Markov random fields

Graphs are fundamental mathematical structures used in various fields to represent data, signals and processes. This paper proposes a novel framework for learning graphs from data. The proposed framework (i) poses the graph learning problem as estimation of generalized graph Laplacian matrices and (ii) develops an efficient algorithm. Under specific statistical assumptions, the proposed formulation leads to modeling attractive Gaussian Markov random fields. Our experimental results show that the proposed algorithm outperforms sparse inverse covariance estimation methods in terms of graph learning performance.
GBST: Separable transforms based on line graphs for predictive video coding

This paper introduces a novel class of transforms, called graph-based separable transforms (GBSTs), based on two line graphs with optimized weights. For the optimal GBST construction, we formulate a graph learning problem to design two separate line graphs using row-wise and column-wise residual block statistics, respectively. We also analyze the optimality of resulting separable transforms for both intra and inter predicted residual block models. Moreover, we show that separable DCT and ADST (DST-7) are special cases of the GBSTs. Our experimental results demonstrate that the proposed optimized transforms outperform 2-D DCT/ADST and separable KLT.
Performance improvement of ultrasonic therapy equipment by modifying the classical transducer design

In ultrasonic therapy equipment, the construction of the transducer is based on a piezoelectric ceramic glued to a metallic protection layer. The protection layer thickness is commonly chosen to be a multiple of the acoustic half-wavelength. This classical transducer design criterion is focused on maximising the energy transfer through this layer. However, it is demonstrated that the optimum thickness protection layer depends not only on the energy transfer through the layer, but also on the complete ultrasonic therapy system. Other factors such as the finite dimensions of the piezoelectric ceramic, the electrical excitation circuitry, and the propagation medium must be taken into account. By using an electrical model to simulate the piezoelectric material, the protective layer, the propagating medium and the excitation generator, and with the aid of electrical simulation programs, it can be concluded that a different layer thickness would be preferable. The performance improvement in ultrasonic therapy equipment is based on the fact that the variation of the protective layer thickness permits modification of the relative values of the electrical impedance of both the generator and the ultrasonic transducer, i.e., is their electrical matching. Theoretical results, obtained by means of simulations based on the electrical models, are in accordance with the experimental measurements of the transducer made with the proposed design.
On Dependent Bit Allocation for Multiview Image Coding With Depth-Image-Based Rendering

The encoding of both texture and depth maps of multiview images, captured by a set of spatially correlated cameras, is important for any 3-D visual communication system based on depth-image-based rendering (DIBR). In this paper, we address the problem of efficient bit allocation among texture and depth maps of multiview images. More specifically, suppose we are given a coding tool to encode texture and depth maps at the encoder and a view-synthesis tool to construct intermediate views at the decoder using neighboring encoded texture and depth maps. Our goal is to determine how to best select captured views for encoding and distribute available bits among texture and depth maps of selected coded views, such that the visual distortion of desired constructed views is minimized. First, in order to obtain at the encoder a low complexity estimate of the visual quality of a large number of desired synthesized views, we derive a cubic distortion model based on basic DIBR properties, whose parameters are obtained using only a small number of viewpoint samples. Then, we demonstrate that the optimal selection of coded views and quantization levels for corresponding texture and depth maps is equivalent to the shortest path in a specially constructed 3-D trellis. Finally, we show that, using the assumptions of monotonicity in the predictor's quantization level and distance, suboptimal solutions can be efficiently pruned from the feasible space during solution search. Experiments show that our proposed efficient selection of coded views and quantization levels for corresponding texture and depth maps outperforms an alternative scheme using constant quantization levels for all maps (commonly used in video standard implementations) by up to 1.5 dB. Moreover, the complexity of our scheme can be reduced by at least 80% over the full solution search.
Distributing OSGi Services: The OSIRIS Domain Connector

This paper presents a way of providing remote capabilities to OSGi services in order to build a distributed environment based on cooperative services. The OSGi Service Platform Specification delivers an open, common architecture for service providers, developers and operators to develop, deploy and manage services in a coordinated fashion. The OSGi Framework forms the core of the OSGi Service Platform Specification. It provides a general-purpose, secure, and managed Java framework whose extensible and downloadable units of deployment, called bundles, can export services and resources (such as packages and classes). However, the mechanisms of synchronization and communication of services allocated in different Service Platforms do not concern to the OSGi Service Platform Specification. This paper presents a new distributed service oriented architecture and its possible industrial applications and it benefits from previous European R&D projects in which the authors have participated.