A Hybrid Image Compression Technique Using Wavelet Transformation - MFOCPN and Interpolation.

In this paper an interpolation method is proposed for compression technique. The method used is the localizing of spatial and frequency correlation from wavelets. Modified Forward Only Counter Propagation Neural Network (MFOCPN) is used for the classification and functional task. The wavelet based technique decomposes the lower sub band consisting of non significant coefficients and are eliminated. The significant smooth and sharp coefficients are found using interpolation methods. Here a new technique is proposed called the cosine interpolation, which is an alternative to the nearest neighborhood interpolation method. This methodology of interpolation proved to be an efficient approach for mapping all significant coefficients and thus resulting in improved quality. Hence the comparison is made between nearest neighborhood interpolation and cosine interpolation. The experimental results are tested on various standard images, where these results yield a better PSNR value compared with the existing nearest neighbor interpolation method

Performance Analysis of Image Scaling Algorithms

Image scaling is the process of resizing a digital image, it is one of the main operations in image processing task such as computer graphics, gaming, medical image processing, virtualization, camera surveillance and quality control. Different image interpolation algorithms are used in resizing digital images. To resize an image, every pixel in the new image must be mapped back to a location in the old image in order to calculate a value of new pixel. There are many algorithms available for determining new value of the pixel, most of which involve some form of interpolation among the nearest pixels in the old image. In this paper, we used Nearest-neighbor, Bilinear, Bicubic, Bicubic B-spline, Catmull-Rom, Lanzcos of order two and Lanzcos of order three algorithms for image interpolation. Each algorithms generates varies artifact such as aliasing, blurring and moiré which results in to different look to the scaled image. This paper presents analysis of quality and computational time consideration of images while using these interpolation algorithms

Validation of geographically based surface interpolation methods for adjusting construction cost estimates by project location

In the construction industry, cost estimates are fundamental to the success of a construction project. Location factors are commonly used to adjust cost estimates by project location. However, not all locations have corresponding factors. Nowadays, the construction industry has employed a simple, proximity-based location factor interpolation method which is widely accepted and used. Under this method, for a location without adjustment factor, the factor of the geographically “nearest neighbor” will be selected. Although this approach was statistically substantiated by former research, it was still not sufficiently supported, considering that only one year’s RSMeans City Cost Index (CCI) dataset was tested. With the help of the Global Moran’s I Test in ArcGIS software, this study evaluated the spatial autocorrelation of the changes in RSMeans CCI value from year 2005 to 2009. The evaluation results substantially supported the validity of the proximity-based location factor interpolation method. In addition, evaluation of current and alternative surface interpolation methods reveals that condition nearest neighbor (CNN) method is the best rough surface interpolation method while inverse distance weighted (IDW) method is the best smooth surface interpolation method. Moreover, the Area Cost Factor (ACF) of the Department of Defense (DoD) was incorporated in this research to cross-validate all evaluations. This research is an initial step for identifying surface interpolation methods to develop spatial prediction models for location adjustment based upon several datasets, including construction cost data and socio-economical data

INDEX QUALITY ASSESMENT CITRA TERINTERPOLASI (SSIM dan FSIM)

Ada sejumlah aplikasi dalam pengenalan pola yang membutuhkan citra dengan ukuran tertentu. Ukuran citra menentukan hasil dari pengenalan pola suatu sistem. Suatu metode interpolasi digunakan untuk menyesuaikan ukuran suatu citra. Kualitas suatu citra terinterpolasi bergantung pada metode interpolasi yang digunakan. Image Quality Assessment (IQA) memainkan suatu peranan penting dalam berbagai aplikasi pengolahan citra seperti peningkatan kualitas citra, kompresi citra, restorasi citra, dan lain sebagainya. IQA sangat dibutuhkan karena suatu citra dapat mengandung beberapa tipe derau seperti derau blur, perubahan kontras dan sebagainya. Pada penelitian ini dibandingkan 4 buah metode interpolasi yang digunakan untuk meningkatkan kualitas citra. Keempat metode tersebut adalah Nearest Neighbor Interpolation (NNI), Bilinear Interpolation, Bicubic Interpolation dan Nearest Neighbor Value Interpolation (NNVI). Metode-metode ini dianalisa dengan IQA. IQA yang digunakan adalah Image Quality Assesment Metrics (SSIM) dan A Feature Similarity Index (FSIM). Metode Bicubic Interpolation menunjukkan nilai yang paling baik untuk PSNR, SSIM dan FSIM. Metode NNVI menunjukkan nilai yang kurang baik dibanding ketiga metode interpolasi lainnya. Metode Bilinear Interpolation dan NNI memberikan kualitas yang berada ditengah-tengah antara metode Bicubic Interpolation dan NNVI

Comparison of Steganography Using the Discrete Cosine Transform Method on Image Based Bilinear, Nearest Neighbor and Spline Interpolation

The research was conducted in the field of steganography. Discrete Cosine Transform (DCT) is a method used in the insertion technique. The results of steganography have problems if they look blurry, have low levels of similarity and high error values. One way to solve this problem is by proposing image interpolation. The interpolation method consists of various kinds and gives each other advantages. This study intends to compare three kinds of interpolation techniques to find the best one. The three interpolation techniques are bilinear, nearest neighbor, and spline. The method used in this research is experimental. Images with extension formats * .tif, * .png, and * .bmp with dimensions of 512x512 px are interpolated by scaling 1.5, 2, and 4. The results of the interpolation process are used to insert messages in * .txt format of 157 bytes with discrete cosines transform (DCT). The image quality of the message insertion is measured by the MSE and PSNR values. The result of the message insertion test shows that the value of the image quality is directly proportional, meaning that if the condition of the message size is fixed and the cover dimensions are greater, the MSE value will be smaller and the PSNR value will be greater. Images with * .tif and * .bmp extension formats have good stability, * .png images vary in size. The smallest error value test results were obtained in the spline interpolation technique and this method when compared to the other two techniques had the lowest average MSE value of 8.221 and the PSNR value of 40,301 dB