Today's Multimedia Applications Research Articles

Deep learning-based video coding optimisation of H.265

Today's multi-media applications need high video quality with low bitrates. However, it is restricted in its capacity to provide higher quality than earlier coding methods. Deep learning (DL) approaches for video coding have shown compression capacities equal to or better than traditional methods, including high-efficiency video coding (HEVC) methods. The trade-off between compression efficiency and encoding/decoding complexity, optimisation for perceptual nature of semantic dependability, specialisation, and universality, the federalised layout of various deep toolkits, etc. remains unclear. HEVC encoding is more efficient than previous standards. Improved efficiency is driven by intra image prediction, which incorporates more prior directions (35 modes) than previous standards. Its high efficiency comes from balancing encoder complexity and dependability. This article presents DL, which uses a convolutional neural network to predict the best model with the least rate-distortion (RD) and further promotes study into deep learning video coding (DLVC).

A METHOD FOR INTEGRATING MULTIMEDIA METADATA STANDARDS AND METADATA FORMATS WITH THE MULTIMEDIA METADATA ONTOLOGY

The M3O abstracts from the existing metadata standards and formats and provides generic modeling solutions for annotations, decompositions, and provenance of metadata. Being a generic modeling framework, the M3O aims at integrating the existing metadata standards and metadata formats rather than replacing them. This is in particular useful as today's multimedia applications often need to combine and use more than one existing metadata standard or metadata format at the same time. However, applying and specializing the abstract and powerful M3O modeling framework in concrete application domains and integrating it with existing metadata formats and metadata standards is not always straightforward. Thus, we have developed a step-by-step alignment method that describes how to integrate existing multimedia metadata standards and metadata formats with the M3O in order to use them in a concrete application. We demonstrate our alignment method by integrating seven different existing metadata standards and metadata formats with the M3O and describe the experiences made during the integration process.

Optimizing data structures at the modeling level in embedded multimedia

Traditional design techniques for embedded systems apply transformations on the source code to optimize hardware-related cost factors. Unfortunately, such transformations cannot adequately deal with the highly dynamic nature of today's multimedia applications. Therefore, we go one step back in the design process. Starting from a conceptual UML model, we first transform the model before refining it into executable code. This paper presents: various model transformations, an estimation technique for the steering cost parameters, and three case studies that show how our model transformations result in factors improvement in memory footprint and performance with respect to the initial implementation.

A Flexible Hardware JPEG 2000 Decoder for Digital Cinema

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> &lt;?Pub _touchup WordspNominal="1.6" WordspMinimum="1.3" WordspMaximum="2.9"?&gt;The image compression standard JPEG 2000 proposes a large set of features that is useful for today's multimedia applications. Unfortunately, it is much more complex than older standards. Real-time applications, such as digital cinema, require a specific, secure, and scalable hardware implementation. In this paper, a decoding scheme is proposed with two main characteristics. First, the complete scheme takes place in a field-programmable gate array without accessing any external memory, allowing integration in a &lt;?Pub /_touchup?&gt;secured system. Second, a customizable level of parallelization allows to satisfy a broad range of constraints, depending on the signal resolution. The resulting architecture is therefore ready to meet upcoming digital cinema specifications. </para>

Texture characterization for joint compression and classification based on human perception in the wavelet domain

Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented.

Token bucket characterization of long-range dependent traffic

The token bucket characterization provides a deterministic yet concise representation of a traffic source. In this paper, we study the impact of the long-range dependence (LRD) property of traffic generated by today's multimedia applications on the optimal dimensioning of token bucket parameters. To this aim, we empirically illustrate the difference between the token bucket characteristics of traffic exhibiting different degrees of time dependence but with identical macroscopic properties (i.e. inter-arrival time and packet size distributions). In addition, we use a statistical model to analytically determine optimal token bucket parameters under various optimization criteria. The statistical model is based on fractional Brownian motion and takes LRD into account. We apply this model to several aggregated MPEG video sources. We then assess the validity of these analytic results by comparing them to empirical results. We conclude that the analytic approach presented here is effective in optimally sizing token buckets for LRD traffic, and promises to be applicable under different traffic conditions and for various optimization criteria.

Arbitrarily shaped transform coding based on a new padding technique

Coding of arbitrarily shaped image segments is an important tool to achieve object-based coding, which is becoming more and more popular in today's multimedia applications. We introduce a new padding technique based on which the arbitrarily shaped DCT can be implemented using a normal N/spl times/N DCT. The new padding is carried out for each arbitrarily shaped block in such a way that there are as many transformed coefficients of high frequencies as possible that could be set to zero. In the best case, it does not expand the data set in the DCT-domain. Arbitrarily shaped DCT coding based on this padding technique is developed, and then analyzed and compared against some of the existing algorithms in terms of the rate-distortion performance, computational complexity, and implementation cost.