Multiresolution Histogram Analysis for Color Reduction

Ramella, Giuliana; Baja, Gabriella Sanniti di

doi:10.1007/978-3-642-16687-7_8

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…In Table 1, the main features of the benchmark methods are reported. Note that various other datasets, often employed in other image analysis tasks (e.g., color quantization [45][46][47][48][49][50][51] and image segmentation [52][53][54][55]) were employed in [21] to evaluate the performance of the benchmark methods. Moreover, some of the benchmark methods had limitations that were either overcome without altering the method itself too much or resulted in their inability to be used in some experiments (see Sections 2.3 and 3).…”

Section: Benchmark Methodsmentioning

confidence: 99%

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Ramella

Themistoclakis

2023

Remote Sensing

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Image resizing (IR) has a crucial role in remote sensing (RS), since an image’s level of detail depends on the spatial resolution of the acquisition sensor; its design limitations; and other factors such as (a) the weather conditions, (b) the lighting, and (c) the distance between the satellite platform and the ground targets. In this paper, we assessed some recent IR methods for RS applications (RSAs) by proposing a useful open framework to study, develop, and compare them. The proposed framework could manage any kind of color image and was instantiated as a Matlab package made freely available on Github. Here, we employed it to perform extensive experiments across multiple public RS image datasets and two new datasets included in the framework to evaluate, qualitatively and quantitatively, the performance of each method in terms of image quality and statistical measures.

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Section: Benchmark Methodsmentioning

confidence: 99%

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Ramella

Themistoclakis

2023

Remote Sensing

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“…In particular, although the CQ process is considered a fundamental process for color image analysis and a significant amount of research has been done on CQ, as mentioned above, IQA for CQ degradation has received little attention. Indeed, CQ [41,42,43,44,45,46,47,48,49,50] is an important step in compression methods as improper quantization can produce distortion so reducing the visual quality of the image. In the past, due to the limitations of the display hardware and to the bandwidth restrictions of computer networks, the main applications of CQ were image display [42,51] and image compression [52,53].…”

Section: Color Quantization Distortionmentioning

confidence: 99%

Evaluation of quality measures for color quantization

Ramella

2020

Preprint

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Visual quality evaluation is one of the challenging basic problems in image processing. It also plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods focused on images corrupted by common degradation types while little attention was paid to color quantization. This in spite there is a wide range of applications requiring color quantization assessment being used as a preprocessing step when color-based tasks are more efficiently accomplished on a reduced number of colors. In this paper, we propose and carry-out a quantitative performance evaluation of nine well-known and commonly used full-reference image quality assessment measures. The evaluation is done by using two publicly available and subjectively rated image quality databases for color quantization degradation and by considering suitable combinations or subparts of them. The results indicate the quality measures that have closer performances in terms of their correlation to the subjective human rating and show that the evaluation of the statistical performance of the quality measures for color quantization is significantly impacted by the selected image quality database while maintaining a similar trend on each database. The detected strong similarity both on individual databases and on databases obtained by integration provides the ability to validate the integration process and to consider the quantitative performance evaluation on each database as an indicator for performance on the other databases. The experimental results are useful to address the choice of suitable quality measures for color quantization and to improve their future employment.

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“…Image dependent methods generally provide good results, but are a bit more expensive. Image dependent methods can be divided in preclustering methods, e.g., (Heckbert, 1982), (Gervautz and Purgathofer, 1990), (Wu, 1992), (Kanjanawanishkul and Uyyanonvara, 2005), and post-clustering methods, e.g., (Ozdemir and Akarun, 2002), (Bing et al, 2004), (Kim and Kehtarnavaz, 2005), (Atsalakis and Papamarkos, 2006), (Chen et al, 2008), (Ramella and Sanniti di Baja, 2010), (Rasti et al, 2011), (Celebi 2011). Pre-clustering methods determine only once the color palette by using features derived from the image at hand.…”

Section: Introductionmentioning

confidence: 99%

Color Quantization via Spatial Resolution Reduction

Ramella¹,

Baja

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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A color quantization algorithm is presented, which is based on the reduction of the spatial resolution of the input image. The maximum number of colors n f desired for the output image is used to fix the proper spatial resolution reduction factor. This is used to build a lower resolution version of the input image with size n f. Colors found in the lower resolution image constitute the palette for the output image. The three components of each color of the palette are interpreted as the coordinates of a voxel in the 3D discrete space. The Voronoi Diagram of the set of voxels corresponding to the colors of the palette is computed and is used for color mapping of the input image.

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Multiresolution Histogram Analysis for Color Reduction

Cited by 4 publications

References 15 publications

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Evaluation of quality measures for color quantization

Color Quantization via Spatial Resolution Reduction

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