The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

State-of-the-art lossless image compression schemes, such as JPEG-LS and CALIC, have been proposed in the context, have been proposed in the context-adaptive predictive coding framework. The essential components of the framework are prediction followed by entropy coding of the resulting residuals. In the framework, an image is encoded in a predefined order, such as, the raster scan order. It relies on the observation that the intensity value of a pixel is highly correlated with its previously encoded neighboring pixels and thus they can be used to make an effective prediction for the current pixel. The beneficial effect of prediction is that the zero-order entropy of the residual is significantly lower than that of the original image. Therefore, encoding of the residuals, instead of the original images, using entropy coding, such as Huffman codes and arithmetic codes, yields better compression in practice.Entropy coding of the residuals requires knowledge of their probability distribution. It can be observed that the distribution of residuals depends on the image's activity

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“…Let I be an image as defined (1). Let v i , j ,k ∈ {0, 1} be the kth significant bit in the b-bit binary representation of v i , j .…”

Section: A) Bit Plane Decomposition (Bpd)mentioning

confidence: 99%

“…State-of-the-art lossless image compression algorithms, such as, JPEG-LS [1], CALIC [2], and EDP [3], have been proposed in the context-adaptive predictive coding framework. The essential components of the framework are prediction followed by entropy coding of the resulting residuals.…”

Section: Introductionmentioning

confidence: 99%

Lossless image coding using hierarchical decomposition and recursive partitioning

Ali

Murshed

Shahriyar

et al. 2016

SIP

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“…We present the results as the bit-rates obtained with the algorithm on the set of popular test images used in JPEG standard benchmarking. First column shows the results of a JPEG-LS standard coder [6], second column shows the results of the JPEG 2000 coder with the reversible wavelet transform [20], next two columns gives the results of the CALIC scheme [3] and a TMW scheme [9] respectively. The last column shows the results of our proposed CBPC scheme.…”

Section: Compression Efficiencymentioning

confidence: 99%

“…Prediction accuracy is crucial in image decorrelation, and therefore has been the main subject of extensive research in the field. Some algorithms, such as CALIC [3], or JPEG-LS [6], use simple static or switching predictors and therefore lack the robustness in the cases of abrupt changes in image region properties, or more complex image structures such as nontrivial edges, textures with complex properties etc. Other proposals deploy highly adaptive predictors such as the least squares based predictors [5,7], neural networks [8], two pass linear prediction [9] 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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“…Today the most commonly used formats are JPG LS [1], and JPEG2000 [2], due to it's speed and sufficiently good compression ratios. Significantly, more complex algorithms like CALIC [3], TMW [5] and EDP [4] have been developed for improved compression ratios.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Lossless Prediction based Image Compression

Jovanović¹,

Lorentz²

2014

Appl. Math. Inf. Sci.

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Predictive methods of image compression traditionally visit the pixels to be compressed in raster scan order, making a prediction for that pixel and storing the difference between the pixel and its prediction. We introduce a new predictive lossless compression method in which the order in which the pixels are visited is determined using a predictor based on previously known pixel values. This makes it possible to reconstruct the image without storing this path. In our tests on standard benchmark images; we show that our approach gives a significant improvement to row wise use of one or two dimensional predictors and gives results similar to or better than standard compression algorithms like median compression and JPG 2000.

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The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

Cited by 1,369 publications

References 37 publications

Lossless image coding using hierarchical decomposition and recursive partitioning

Lossless image coding using hierarchical decomposition and recursive partitioning

Lossless Image Compression Exploiting Streaming Model for Efficient Execution on Multicores

Adaptive Lossless Prediction based Image Compression

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