Interesting properties of the discrete cosine transform

Hou, H. S.; Tretter, Daniel; Vogel, Mathias

doi:10.1016/1047-3203(92)90031-n

Cited by 12 publications

(4 citation statements)

References 39 publications

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“…Among the possible spectral related parameters, we chosen the Discrete Cosine Transform (DCT) representation. DCT is a well-known signal processing operation with important properties [25]. For example, it is useful for reducing the signal redundancy since it places as much energy as possible in as few coefficients as possible (energy compaction).…”

Section: Data Analysis Methodologymentioning

confidence: 99%

Cloud-Based Machine Learning Tools for Enhanced Big Data Applications

Cuzzocrea

Mumolo

Corona

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the "next" workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combine the effectiveness of different well-known classifiers in order to enhance the whole accuracy of the final classification, which is very relevant at now in the specific context of Big Data. So-called workload categorization problem plays a critical role towards improving the efficiency and the reliability of Cloudbased big data applications. Implementation-wise, our method proposes deploying Cloud entities that participate to the distributed classification approach on top of virtual machines, which represent classical "commodity" settings for Cloud-based big data applications. Preliminary experimental assessment and analysis clearly confirm the benefits deriving from our classification framework. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

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Section: Data Analysis Methodologymentioning

confidence: 99%

Cloud-Based Machine Learning Tools for Enhanced Big Data Applications

Cuzzocrea

Mumolo

Corona

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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“…We now describe how this work can be adapted to ripmapping. For example, Martucci [14] and Hou et al [10] investigated the spectral properties and impulse response of the low pass filters implied by dropping AC-coefficients and Dugad et al [6] proposed a practical system to transcode video sequences based on this. The main contribution of these references is that by dropping the upper half of the AC coefficients in the compressed domain we can get high-quality artifact free versions of the source texture downsampled by a factor of two.…”

Section: Transform Domain Minification Filteringmentioning

confidence: 99%

A new approach to combine texture compression and filtering

Hollemeersch¹,

Pieters²,

Lambert³

et al. 2011

Vis Comput

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Texture mapping has been widely used to improve the quality of 3D rendered images. To reduce the storage and bandwidth impact of texture mapping, compression systems are commonly used. To further increase the quality of the rendered images, texture filtering is also often adopted. These two techniques are generally considered to be independent. First, a decompression step is executed to gather texture samples, which is then followed by a separate filtering step. We have investigated a system based on linear transforms that merges both phases together. This allows more efficient decompression and filtering at higher compression ratios. This paper formally presents our approach for any linear transformation, how the commonly used discrete cosine transform can be adapted to this new approach, and how this method can be implemented in real time on current-generation graphics cards using shaders. Through reuse of the existing hardware filtering, fast magnification and minification filtering is achieved. Our implementation provides fully anisotropically filtered samples four to six times faster than an implementation using two separate phases for decompression and filtering. Additionally, our transform-based compression also provides increased and C.-F. Hollemeersch ( ) · B. Pieters · P. Lambert · R. Van de Walle Multimedia Lab., Gaston Crommenlaan 8 bus 201, variable compression ratios over standard hardware compression systems at a comparable or better quality level.

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“…Among the possible spectral related parameters, we chosen the Discrete Cosine Transform (DCT) representation. DCT is a well-known signal processing operation with important properties [21]. For example, it is useful for reducing the signal redundancy since it places as much energy as possible in as few coefficients as possible (energy compaction).…”

Section: Fig 4 Analysis Framesmentioning

confidence: 99%

Coarse-grained workload categorization in virtual environments using the Dempster-Shafer fusion

Cuzzocrea

Mumolot

Corona

2015

2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Given a number of known reference workloads, and an unknown workload, this paper deals with the problem of finding the reference workload which is most similar to the unknown one. T he depicted scenario turns to be useful in a plethora of modern information system applications. We\ud name this problem as coarse-grained workload classification, because, instead of characterizing the unknown workload in terms of finer behaviors, such as CPU, memory, disk or network intensive patterns, we classify the whole unknown workload as one of the (possible) reference workloads. Reference workloads represent a category of workloads that are relevant in a given applicative environment. In particular, we focus our attention\ud on the classification problem described above in the special case represented by virtualized environments. Today, Virtual Machines (VMs) have become very popular because they offer important advantages to modern computing environments such as cloud computing or server farms. In virtualization frameworks, workload classification is very useful for accounting, security reasons or user profiling. Hence, our research makes more sense in such environments, and it turns to be very useful in a special context like cloud computing, which is emerging at now. In this respect, our approach consists in running several machine-learning-based classifiers of different workload models, and then deriving the best classifier produced by the Dempster-Shafer fusion, in order to magnify the accuracy of the final classification. Experimental assessment and analysis c1ealry confirm the benefits deriving from our classification framework

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Interesting properties of the discrete cosine transform

Cited by 12 publications

References 39 publications

Cloud-Based Machine Learning Tools for Enhanced Big Data Applications

Cloud-Based Machine Learning Tools for Enhanced Big Data Applications

A new approach to combine texture compression and filtering

Coarse-grained workload categorization in virtual environments using the Dempster-Shafer fusion

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