Kohonen neural networks for optimal colour quantization

Dekker, Anthony H.

doi:10.1088/0954-898x_5_3_003

Cited by 120 publications

(51 citation statements)

References 8 publications

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“…It has been noted that their complexity ranged from 60 to 80% of the total complexity of video encoding (MPEG-4) [17]. Also, we have used the 1-D Wavelet transformation [20], the Cavity Detector [21] and the Self Organized Feature Map Color Quantization (CQ) [22]. We assumed L1 instruction cache memory size ranging from 64 bytes to 1024 bytes with block sizes 8 and direct-mapped cache architecture and L2 instruction cache with sizes varying between 128 bytes and 4 Kbytes.…”

Section: Comparison Resultsmentioning

confidence: 99%

Fast Instruction Memory Hierarchy Power Exploration for Embedded Systems

Kroupis¹,

Soudris

2010

IFIP Advances in Information and Communication Technology

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A typical instruction memory design exploration process using simulation tools for various cache parameters is a rather time-consuming process, even for low complexity applications. In order to design a power efficient memory hierarchy of an embedded system, a huge number of system simulations are needed for all the different instruction memory hierarchies, because many cache memory parameters should be explored. Exhaustive search of design space using simulation is too slow procedure and needs hundreds of simulations to find the optimal cache configuration. This chapter provides fast and accurate estimates of a multi-level instruction memory hierarchy. Using a detail methodology for estimating the number of instruction cache misses of the instruction cache levels and power models; we estimate within a reasonable time the power consumption among these hierarchies. In order to automate the estimation procedure, a novel software tool named FICA implements the proposed methodology, which automatically estimates the total energy in instruction memory hierarchy and reports the optimal one.

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Section: Comparison Resultsmentioning

confidence: 99%

Fast Instruction Memory Hierarchy Power Exploration for Embedded Systems

Kroupis¹,

Soudris

2010

IFIP Advances in Information and Communication Technology

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“…The original approaches mainly utilized clustering in color space. Representative clustering approaches have been based on median-cut [2], octrees [3], self-organizing maps [4], minmax [10], k-means [1], fuzzy c-means [11], adaptive distributing units [12], and variance-cut based on Lloyd-Max iterations [13].…”

Section: A Color Quantizationmentioning

confidence: 99%

“…Some of these methods use specific data structures to divide the color space, such as median-cut [2], or octrees [3]. Others use iterative clustering methods, such as k-means [1] self-organizing maps [4], to organize all colors into a limited number of clusters. All colors in the same cluster are then represented using the same color, resulting in a quantized representation.…”

Section: Introductionmentioning

confidence: 99%

Efficient, Edge-Aware, Combined Color Quantization and Dithering

Huang

Martin

et al. 2016

IEEE Trans. on Image Process.

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Abstract-In this paper we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified mediancut method. Compared to previous approaches, our method can produce high quality results with fewer visual artifacts but also requires significantly less computational effort.Index Terms-Color quantization, dithering, optimizationbased image processing.

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“…In median cut quantisation [2], an iterative procedure repeatedly splits (by a plane through the median point) colour cells into sub-cells. In octree quantisation [3], the colour space is represented as an octree where sub-branches are successively merged to form the palette, while Neuquant [4] employs a onedimensional self-organising Kohonen neural network to generate the colour map.…”

Section: Where R(i J) G(i J) and B(i J)mentioning

confidence: 99%

“…• Neuquant [4]: A one-dimensional self-organising Kohonen neural network is applied to generate the colour map.…”

Section: Colour Quantisation Performancementioning

confidence: 99%

Soft computing-based colour quantisation

Schaefer

2014

J Image Video Proc

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Soft computing techniques have shown much potential in a variety of computer vision and image analysis tasks. In this paper, an overview of recent soft computing approaches to the colour quantisation problem is presented. Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Those selected colours form a colour palette, while the resulting image quality is directly determined by the choice of colours in the palette. The use of generic optimisation techniques such as simulated annealing and soft computing-based clustering algorithms founded on fuzzy and rough set ideas to formulate colour quantisation algorithms is discussed. These methods are capable of deriving good colour palettes and are shown to outperform standard colour quantisation techniques in terms of image quality. Furthermore, a hybrid colour quantisation algorithm which combines a generic optimisation approach with a common clustering algorithm is shown to lead to improved image quality. Finally, it is demonstrated how optimisation-based colour quantisation can be employed in conjunction with a more appropriate measure for image quality.

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Kohonen neural networks for optimal colour quantization

Cited by 120 publications

References 8 publications

Fast Instruction Memory Hierarchy Power Exploration for Embedded Systems

Fast Instruction Memory Hierarchy Power Exploration for Embedded Systems

Efficient, Edge-Aware, Combined Color Quantization and Dithering

Soft computing-based colour quantisation

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