Classification and Blending Prediction for Lossless Image Compression

Knezović, Josip; Kovač, Mario; Mlinarić, Hrvoje

doi:10.1109/melcon.2006.1653144

Cited by 8 publications

(15 citation statements)

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“…In this section we briefly describe our proposed CBPC compression method, details can be found elsewhere [1,16]. Fig.…”

Section: Cbpc Compression Methodsmentioning

confidence: 99%

“…Past and recent research on lossless compression for images has focused on the predictive contextual modeling in order to extract redundancy present in typical images [1][2][3][4][5]. Image is first treated by a prediction phase where pixels are predicted based on some information about source, such as correlation among neighboring pixels, edges, planar structures, textures etc.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we concentrate on performance improvements of our proposed lossless image compression algorithm named CBPC after the term Classification and Blending Predictive Coding algorithm [1]. While our previous publications on CBPC compression concentrate on the problem of highly adaptive predictive coding of images from the viewpoint of achieving high compression ratio, here we address the execution time of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…For image and video processing applications, streaming model of computation showed to be effective in tackling these challenges. In this paper, we report the efforts to improve the execution performance of the CBPC, our compute intensive lossless image compression algorithm described in [1]. The algorithm is based on highly adaptive and predictive modeling, outperforming many other methods in compression efficiency, although with increased complexity.…”

mentioning

confidence: 99%

“…Nadolaskom višejezgrenih računala pojavljuju se novi izazovi kao što su učinkovito iskorištavanje računskih jezgri i postizanje skalabilnosti rješenja obzirom na povećanje broja jezgri. U ovom radu prikazan je novi pristup poboljšanja izvedbenih performansi metode za kompresiju slika bez gubitaka CBPC koja se odlikuje adaptivnim modelom predvidanja koji omogućuje postizanje boljih stupnjeva kompresije uz povećanje računske složenosti [1]. Pristup koji je primjenjen sastoji se u implementaciji računski zahtjevnog predikcijskog modela u tokovnom programskom jeziku koji omogućuje paralelizaciju izvornog programa.…”

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Lossless Image Compression Exploiting Streaming Model for Efficient Execution on Multicores

2012

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Original scientific paperImage and video coding play a critical role in present multimedia systems ranging from entertainment to specialized applications such as telemedicine. Usually, they are hand-customized for every intended architecture in order to meet performance requirements. This approach is neither portable nor scalable. With the advent of multicores new challenges emerged for programmers related to both efficient utilization of additional resources and scalable performance. For image and video processing applications, streaming model of computation showed to be effective in tackling these challenges. In this paper, we report the efforts to improve the execution performance of the CBPC, our compute intensive lossless image compression algorithm described in [1]. The algorithm is based on highly adaptive and predictive modeling, outperforming many other methods in compression efficiency, although with increased complexity. We employ a high-level performance optimization approach which exploits streaming model for scalability and portability. We obtain this by detecting computationally demanding parts of the algorithm and implementing them in StreamIt, an architecture-independent stream language which goal is to improve programming productivity and parallelization efficiency by exposing the parallelism and communication pattern. We developed an interface that enables the integration and hosting of streaming kernels into the host application developed in general-purpose language. Key words: Lossless image compression, Image coding, Stream programming, Parallel programming, MulticoresKompresija slika bez gubitaka uz iskorištavanje tokovnog modela za izvodenje na višejezgrenim računal-ima. Postupci obrade slikovnih podataka su iznimno zastupljeni u postojećim multimedijskim sustavima, počev od zabavnih sustava pa do specijaliziranih aplikacija u telemedicini. Vrločesto, zbog svojih računskih zahtjeva, ovi programski odsječci su iznimno optimirani i to na niskoj razini, što predstavlja poteškoće u prenosivosti i skalabilnosti konačnog rješenja. Nadolaskom višejezgrenih računala pojavljuju se novi izazovi kao što su učinkovito iskorištavanje računskih jezgri i postizanje skalabilnosti rješenja obzirom na povećanje broja jezgri. U ovom radu prikazan je novi pristup poboljšanja izvedbenih performansi metode za kompresiju slika bez gubitaka CBPC koja se odlikuje adaptivnim modelom predvidanja koji omogućuje postizanje boljih stupnjeva kompresije uz povećanje računske složenosti [1]. Pristup koji je primjenjen sastoji se u implementaciji računski zahtjevnog predikcijskog modela u tokovnom programskom jeziku koji omogućuje paralelizaciju izvornog programa. Ovako projektiran predikcijski model može se iskoristiti kroz sučelje koje smo razvili a koje omogućuje pozivanje tokovnih računskih modula i njihovo paralelno izvodenje uz iskorištavanje više jezgri.Ključne riječi: Kompresija slika bez gubitaka, kodiranje slikovnih podataka, tokovni računalni model, paralelni sustavi, višejezgrena računala

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“…In this section we briefly describe our proposed CBPC compression method, details can be found elsewhere [1,16]. Fig.…”

Section: Cbpc Compression Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Lossless Image Compression Exploiting Streaming Model for Efficient Execution on Multicores

2012

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System for Secure Data Exchange in Telemedicine

Kovacevic

Kovač

Knezović

2007

2007 9th International Conference on Telecommunications

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Adaptive context formation for linear prediction of image data

Strutz

2014

2014 IEEE International Conference on Image Processing (ICIP)

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Images are typically non-stationary signals. If prediction is applied in a linear fashion, it must be combined with a technique which takes this characteristic into account. In general, images can be either regarded as piecewise two-dimensional autoregressive processes or they are handled in a block-wise manner.This paper presents a novel prediction technique, which treats the image data as an interleaved sequence generated by multiple sources. The challenge is to de-interleave the sequence and to compute prediction weights for each sub-source separately. The proposed approach adaptively determines the sub-sources based on the textures within the images.The prediction method is incorporated in a framework for lossless image compression. It is based on least-mean-square filtering and achieves prediction-error entropies, which are comparable to those of least-squares approaches. In combination with a dedicated coding algorithm, the proposed approach shows a competitive compression performance for a wide range of different natural images.

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Classification and Blending Prediction for Lossless Image Compression

Cited by 8 publications

References 9 publications

Lossless Image Compression Exploiting Streaming Model for Efficient Execution on Multicores

Lossless Image Compression Exploiting Streaming Model for Efficient Execution on Multicores

System for Secure Data Exchange in Telemedicine

Adaptive context formation for linear prediction of image data

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