On performance of lossless compression for HDR image quantized in color space

Fang, Tao

doi:10.1016/j.image.2009.01.002

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…To compress image data in such a format, application of the conventional image coding techniques independently to each information part by regarding it as a grayscale image with 8-bit depth has been studied [2]. However, it is observed in the mantissa part that different exponent values between adjacent pels cause strong contours, and therefore lower the coding efficiency.…”

Section: Introductionmentioning

confidence: 99%

Lossless Coding of HDR Color Images in a Floating Point Format Using Block-Adaptive Inter-Color Prediction

Kamataki

Kameda

Kita

et al. 2021

IEICE Trans. Inf. & Syst.

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Section: Introductionmentioning

confidence: 99%

Lossless Coding of HDR Color Images in a Floating Point Format Using Block-Adaptive Inter-Color Prediction

Kamataki

Kameda

Kita

et al. 2021

IEICE Trans. Inf. & Syst.

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“…These approaches offer improved compression efficiency with the compromise in extremely high computational requirements. Predictive modeling has also found applications in other image processing areas such as edge detection [10], high dynamic range image compression [11], image archival [12] etc.…”

Section: Introductionmentioning

confidence: 99%

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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Lossless HDR Image Compression by Modulo Encoding

Swamy¹,

Shylashree²

2023

IETE Journal of Research

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On performance of lossless compression for HDR image quantized in color space

Cited by 6 publications

References 6 publications

Lossless Coding of HDR Color Images in a Floating Point Format Using Block-Adaptive Inter-Color Prediction

Lossless Coding of HDR Color Images in a Floating Point Format Using Block-Adaptive Inter-Color Prediction

Lossless Image Compression Exploiting Streaming Model for Efficient Execution on Multicores

Lossless HDR Image Compression by Modulo Encoding

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