Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

http://ahvazdownload.persiangig.com/document/article/39.pdf

A wavelet tour of signal processing

On robust estimation of the location parameter

/pdf/the-scalable-video-coding-extension-of-the-h-264-avc-2pf4w9upmi.pdf

The Scalable Video Coding Extension of the H.264/AVC Standard

We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.

/pdf/deap-a-database-for-emotion-analysis-using-physiological-1b41jp6d2i.pdf

DEAP: A Database for Emotion Analysis ;Using Physiological Signals

http://pancamr.lecture.ub.ac.id/files/2012/02/A-survey-on-vision-based-human-action-recognition_Image-and-Vision-Computing_2010.pdf

A survey on vision-based human action recognition

We propose Deep Lossless Image Coding (DLIC), a full resolution learned lossless image compression algorithm. Our algorithm is based on a neural network combined with an entropy encoder. The neural network performs a density estimation on each pixel of the source image. The density estimation is then used to code the target pixel, beating FLIF in terms of compression rate. Similar approaches have been attempted. However, long run times make them unfeasible for real world applications. We introduce a parallelized GPU based implementation, allowing for encoding and decoding of grayscale, 8-bit images in less than one second. Because DLIC uses a neural network to estimate the probabilities used for the entropy coder, DLIC can be trained on domain specific image data. We demonstrate this capability by adapting and training DLIC with Magnet Resonance Imaging (MRI) images.

Accelerated Deep Lossless Image Coding with Unified Paralleleized GPU Coding Architecture

In this paper we present a method for the estimation of rigid body motion parameters from a monocular image sequence for MPEG-4 applications, such as SNHC face animation. Based on feature extractions in every frame the motion parameters of a human face are estimated with an extended Kalman filter that performs a prediction and correction loop at every timestep. With this recursive structure of the estimation process the temporal redundancies of the motion are taken into account. The non-linear motion equation is linearized at every timestep within the extended Kalman filter and therefore the rotation is not restricted to be small and the motion model can be based on the first frame and must not describe the frame to frame motion. Results are presented which demonstrate the accuracy of our estimation method on synthetic data as well as on a real image sequence where we estimated the motion parameters of a human face.

Estimation of Motion Parameters of a Rigid Body from a Monocular Image Sequence for MPEG-4 Applications

Research in the past years introduced Steered Mixture-of-Experts (SMoE) as a framework to form sparse, edge-aware models for 2D- and higher dimensional pixel data, applicable to compression, denoising, and beyond, and capable to compete with state-of-the-art compression methods. To circumvent the computationally demanding, iterative optimization method used in prior works an autoencoder design is introduced that reduces the run-time drastically while simultaneously improving reconstruction quality for block-based SMoE approaches. Coupling a deep encoder network with a shallow, parameter-free SMoE decoder enforces an efficent and explainable latent representation. Our initial work on the autoencoder design presented a simple model, with limited applicability to compression and beyond. In this paper, we build on the foundation of the first autoencoder design and improve the reconstruction quality by expanding it to models of higher complexity and different block sizes. Furthermore, we improve the noise robustness of the autoencoder for SMoE denoising applications. Our results reveal that the newly adapted autoencoders allow ultra-fast estimation of parameters for complex SMoE models with excellent reconstruction quality, both for noise free input and under severe noise. This enables the SMoE image model framework for a wide range of image processing applications, including compression, noise reduction, and super-resolution.

Steered mixture-of-experts autoencoder design for real-time image modelling and denoising

In this paper, we present a classification and retrieval technique targeted for retrieval of home video abstract using dimension-reduced, decorrelated spectral features of audio content. The feature extraction based on MPEG-7 descriptors consists of three main stages: Normalized Audio Spectrum Envelope (NASE), basis decomposition algorithm and basis projection, obtained by multiplying the NASE with a set of extracted basis functions. A classifier based on continuous hidden Markov models is applied. For retrieval with accurate performance the system consists of a two-level hierarchy method using speech recognition and sound classification. For the measure of the performance we compare the classification results of MPEG-7 standardized features vs. Mel-scale Frequency Cepstrum Coefficients (MFCC). Results show that the MFCC features yield better performance compared to MPEG-7 features.

Performance of MPEG-7 spectral basis representations for retrieval of home video abstract

Multimedia information retrieval (MIR) and delivery plays an important role in many application domains due to the increasing need to identify, filter, and manage growing amounts of data, notably multimedia information. To efficiently manage and exchange multimedia information, interoperability between coded data and metadata is required and standardization is central to achieving the necessary level of interoperability. In the context of this paper, the term retrieval refers to the process by which a user, human or machine, identifies the content it needs, and the term delivery refers to the adaptive transport and consumption of the identified content in a particular context or usage environment. Both the retrieval and delivery processes may require content and context metadata. This paper will argue that maximum quality of experience depends not only on the content itself (and thus content metadata) but also on the consumption conditions (thus context metadata). Additionally, the rights and protection conditions have become critically important in recent years, especially with the explosion of electronic music commerce and different Bshopping( conditions. This paper will review existing multimedia standards related to information retrieval and adaptive delivery of multimedia content, empha- sizing the need for such standards, and will show how these standards can help the development, dissemination, and valorization of MIR research results. Moreover, it will also discuss limitations of the current standards and anticipate what future standardization activities are relevant and needed. Due to space limitations, the paper will mainly concentrate on MPEG standards although many other relevant standards are also reviewed and discussed.

http://ieeexplore.ieee.org/iel5/5/4472076/04472087.pdf

Multimedia Retrieval and Delivery: Essential Metadata Challenges and Standards To allow reduced costs, technical competition and evolution, and development of sizeable markets, standards are needed for metadata about the nature, production, management and use of multimedia material.

Thomas Sikora

Papers

Accelerated Deep Lossless Image Coding with Unified Paralleleized GPU Coding Architecture

Estimation of Motion Parameters of a Rigid Body from a Monocular Image Sequence for MPEG-4 Applications

Steered mixture-of-experts autoencoder design for real-time image modelling and denoising

Performance of MPEG-7 spectral basis representations for retrieval of home video abstract

Multimedia Retrieval and Delivery: Essential Metadata Challenges and Standards To allow reduced costs, technical competition and evolution, and development of sizeable markets, standards are needed for metadata about the nature, production, management and use of multimedia material.