Soft computing-based colour quantisation

Schaefer, Gerald

doi:10.1186/1687-5281-2014-8

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…1 , namely Lenna, Peppers, Mandrill, Sailboat, Airplane, and Pool. In all experiments, the number of colours in the palette is set to 16 [ 8 ] and we set , , K (the number of clusters during grouping) to 5, , , and . HMS is run with a population size of 50 for 500 iterations.…”

Section: Resultsmentioning

confidence: 99%

“…It can be observed that the octree and Neuquant algorithms outperform the popularity and median cut approaches. Furthermore, it is clear that the soft computing-based colour quantisation algorithms outperform octree and Neuquant confirming that methods based on soft computing techniques are effective tools for colour quantisation [ 8 ].…”

Section: Resultsmentioning

confidence: 99%

“…In median cut quantisation [ 4 ], the colour space is divided into sub-spaces in an iterative process, while in [ 3 ], the colour space is represented as an octree whose sub-branches combine to form the pallete. Self-organising Kohonen neural networks have also been proposed [ 2 ], as have a number of soft computing methods [ 8 ] such as simulated annealing [ 10 ], fuzzy c-means [ 11 ], rough c-means [ 12 ], and fuzzy-rough c-means [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Colour Quantisation by Human Mental Search

Mousavirad

Schaefer

Fang

et al. 2020

Lecture Notes in Computer Science

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Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image which are then represented by a colour palette. The selection of appropriate entries in this palette is a challenging issue while the quality of the quantised image is directly related to the colour palette. In this paper, we propose a novel colour quantisation algorithm based on the human mental search (HMS) algorithm. HMS is a recent population-based metaheuristic algorithm with three main operators: mental search to explore the vicinity of candidate solutions based on Levy flight, grouping to determine a promising region based on a clustering algorithm, and movement towards the best strategy. The performance of our proposed algorithm is evaluated on a set of benchmark images and in comparison to four conventional algorithms and seven soft computing-based colour quantisation algorithms. The obtained experimental results convincingly show that our proposed algorithm is capable of outperforming these approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Colour Quantisation by Human Mental Search

Mousavirad

Schaefer

Fang

et al. 2020

Lecture Notes in Computer Science

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“…This process involves separating zeros from nonzero data, as shown in Figure-3.The zero-array can be computed easily by calculating the number of zeros between two nonzero data. For example, assume the following AC-Matrix=[0.5, 0, 0, 0, 7.3, 0, 0, 0, 0, 0, -7], the zero-array will be [0, 3,0,5,0] where the zeros in red refer to nonzero data existing at these positions in the original AC-Matrix and the numbers in black refer to the number of zeros between two consecutive non-zero data. In order to increase the compression ratio, the number "5" in the zeroarray can be broken up into "3" and "2"zeros to increase the probability (i.e.…”

Section: Two Level Discrete Cosine Transform (Dct)mentioning

confidence: 99%

A Novel 2D Image Compression Algorithm Based on Two Levels DWT and DCT Transforms with Enhanced Minimize-Matrix-Size Algorithm for High Resolution Structured Light 3D Surface Reconstruction

Siddeq

Rodrigues

2015

3D Res

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This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. SIDDEQ, M and RODRIGUES, Marcos (2015). A novel 2D image compression algorithm based on two levels DWT and DCT transforms with enhanced minimizematrix-size algorithm for high resolution structured light 3D surface reconstruction. 3D Research, 6 (3), p. 26. Published version Copyright and re-use policy AbstractImage compression techniques are widely used in 2D and 3D image and video sequences. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level Discrete Wavelet Transform (DWT) and a two level Discrete Cosine Transform (DCT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of 4 steps: 1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC-and AC-Matrix, or low and high frequency matrix respectively; 2) apply a second level DCT to the DC-Matrix to generate two arrays, namely nonzero-array and zero-array; 3) apply the Minimize-Matrix-Size (MMS) algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT; 4) apply arithmetic coding to the output of previous steps. A novel Fast-Match-Search (FMS) decompression algorithm is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined into one matrix followed by inverse two level DCT with two level DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D RMSE following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D.

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“…Pixels associated with a certain area, commonly described based on features such as colour or texture, exhibit more commonalities than those assigned to different areas. Image segmentation is employed in a variety of applications, such as satellite image analysis [1,2], colour quantisation [3], tumour detection [4], food quality evaluation [5], and modelling of microstructures [6], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Effective image clustering based on human mental search

Mousavirad

Ebrahimpour-Komleh

Schaefer

2019

Applied Soft Computing

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Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as k-means, can be used for this purpose but have several drawbacks including dependence on initialisation conditions and a higher likelihood of converging to local rather than global optima. In this paper, we propose a clustering-based image segmentation method that is based on the human mental search (HMS) algorithm. HMS is a recent metaheuristic algorithm based on the manner of searching in the space of online auctions. In HMS, each candidate solution is called a bid, and the algorithm comprises three major stages: mental search, which explores the vicinity of a solution using Levy flight to find better solutions; grouping which places a set of candidate solutions into a group using a clustering algorithm; and moving bids toward promising solution areas. In our image clustering application, bids encode the cluster centres and we evaluate three different objective functions. In an extensive set of experiments, we compare the efficacy of our proposed approach with several state-of-the-art metaheuristic algorithms including a genetic algorithm, differential evolution, particle swarm optimisation, artificial bee colony algorithm, and harmony search. We assess the techniques based on a variety of metrics including the objective functions, a cluster validty index, as well as unsupervised and supervised image segmentation criteria. Moreover, we perform some tests in higher dimensions, and conduct a statistical analysis to compare our proposed method to its competitors. The obtained results clearly show that the proposed algorithm represents a highly effective approach to image clustering that outperforms other state-of-the-art techniques.

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Soft computing-based colour quantisation

Cited by 9 publications

References 31 publications

Colour Quantisation by Human Mental Search

Colour Quantisation by Human Mental Search

A Novel 2D Image Compression Algorithm Based on Two Levels DWT and DCT Transforms with Enhanced Minimize-Matrix-Size Algorithm for High Resolution Structured Light 3D Surface Reconstruction

Effective image clustering based on human mental search

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