scholarly journals Bit-Error Aware Lossless Image Compression with 2D-Layer-Block Coding

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Jungan Chen ◽  
Jean Jiang ◽  
Xinnian Guo ◽  
Lizhe Tan
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Communication Systems ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Statistical Correlation ◽  
Mean Values ◽  
Lossless Image Compression ◽  
Layer Extraction

With IoT development, it becomes more popular that image data is transmitted via wireless communication systems. If bit errors occur during transmission, the recovered image will become useless. To solve this problem, a bit-error aware lossless image compression based on bi-level coding is proposed for gray image compression. But bi-level coding has not considered the inherent statistical correlation in 2D context region. To resolve this shortage, a novel variable-size 2D-block extraction and encoding method with built-in bi-level coding for color image is developed to decrease the entropy of information and improve the compression ratio. A lossless color transformation from RGB to the YCrCb color space is used for the decorrelation of color components. Particularly, the layer-extraction method is proposed to keep the Laplacian distribution of the data in 2D blocks which is suitable for bi-level coding. In addition, optimization of 2D-block start bits is used to improve the performance. To evaluate the performance of our proposed method, many experiments including the comparison with state-of-the-art methods, the effects with different color space, etc. are conducted. The comparison experiments under a bit-error environment show that the average compression rate of our method is better than bi-level, Jpeg2000, WebP, FLIF, and L3C (deep learning method) with hamming code. Also, our method achieves the same image quality with the bi-level method. Other experiments illustrate the positive effect of built-in bi-level encoding and encoding with zero-mean values, which can maintain high image quality. At last, the results of the decrease of entropy and the procedure of our method are given and discussed.

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

scholarly journals Compression of image using hybrid combination of haar wavelet transform and color space model in noisy environment

2017 ◽  
Vol 7 (1.5) ◽  
pp. 283 ◽  
Author(s):  
Sridhar C S ◽  
Mahadevan G ◽  
S.K. Khadar Basha ◽  
G. ChenchuKrishnaiah
Keyword(s):  
Image Compression ◽  
Color Image ◽  
Color Space ◽  
Haar Wavelet ◽  
Noisy Environment ◽  
Haar Wavelet Transform ◽  
Security Issues ◽  
Grey Scale ◽  
Space Transformation ◽  
Color Space Transformation

This article manages the examination of image compression utilizing crossover blend of both wavelet change and color space.. The paper covers foundation which incorporates decay of image, thresholding in wavelet for image compression. The compressed image of Haar wavelet is then gone through color space transformation which is utilized to change the color image into grey scale image. On transformation to grey scale image, the number of bits and transfer speed gets diminished. It is conceivable to process the images of various organizations inside the security issues that have been additionally discussed and actualized. The outcomes are shown as depictions in the results. 

Enhancement of Nonuniformly Illuminated Underwater Images

2020 ◽  
pp. 2154008
Author(s):  
Monika Mathur ◽  
Nidhi Goel
Keyword(s):  
Image Quality ◽  
Color Image ◽  
Light Attenuation ◽  
Color Space ◽  
Discrete Wavelet ◽  
Underwater Image ◽  
Image Capturing ◽  
Contrast Stretching ◽  
Result Analysis ◽  
Image Details

Underwater image capturing is a challenging task due to attenuation of light in water. Scattering and absorption are the results of light attenuation which lead to faded colors and reduced contrast of images, respectively. To deal with these issues and to provide better visual quality image, various enhancement methods have been proposed. This paper proposes the Dual Domain-based Underwater Image Enhancement (DDUIE) method. DDUIE method provides contrast stretching in approximation band of discrete wavelet transformed image followed by intensity adjustment of different color channels in spatial domain. To further improve the color quality, the image is processed in HSV (Hue–Saturation–Value) color space. Result analysis indicates better results for DDUIE method over state-of-the-art methods. Subjective results of DDUIE method show minimization of the bluish-green effect and reduction of nonuniform illumination up to a certain extent. These lead to enhanced color and image details. Quantitative results show that the Underwater Image Quality Measure (UIQM) and Underwater Color Image Quality Evaluation (UCIQE) values between 1 and 2 and between 0 and 1 have been achieved, respectively, which significantly illustrate that images have been enhanced efficiently and also entropy values between 7 and 8 depict the effectiveness of the proposed method in terms of image details.

scholarly journals Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

2021 ◽  
Vol 38 (2) ◽  
pp. 281-289
Author(s):  
Ahmed Bouida ◽  
Mustapha Khelifi ◽  
Mohammed Beladgham ◽  
Fatima-Zohra Hamlili
Keyword(s):  
Monte Carlo ◽  
Image Quality ◽  
Image Compression ◽  
Image Quality Assessment ◽  
Image Data ◽  
Optimization Method ◽  
Data Systems ◽  
Monte Carlo Optimization ◽  
Compressed Images ◽  
Quality Aspects

In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate and qualify this quality, we investigate the use of textural combined image quality metrics (TCQ) based on the fusion of full reference structural, textural, and edge evaluation metrics. To optimize this metric, we use the Monte Carlo optimization method. This approach allows us to qualify our compressed images and propose the best metric that evaluates compressed images according to several textural quality aspects.

Image Compression and Reconstruction based on Fuzzy Relation and Soft Computing Technology

2004 ◽  
Vol 8 (1) ◽  
pp. 72-80
Author(s):  
Kaoru Hirota ◽  
◽  
Hajime Nobuhara ◽  
Kazuhiko Kawamoto ◽  
Shin’ichi Yoshida
Keyword(s):  
Image Reconstruction ◽  
Image Compression ◽  
Soft Computing ◽  
Signal To Noise Ratio ◽  
Color Space ◽  
Image Data ◽  
Compression Rate ◽  
Reconstruction Method ◽  
Image Reconstruction Method ◽  
Fuzzy Relational Equation

A fast image reconstruction method for Image Compression method based on Fuzzy relational equation (ICF) and soft computing is proposed. In experiments using 20 images (Standard Image DataBAse), the decrease in image reconstruction time to 1/132.02 and 1/382.29 are obtained when the compression rate is 0.0156 and 0.0625, respectively, and the proposed method outperforms the conventional one in the Peak Signal to Noise Ratio (PSNR). ICF using nonuniform coders over YUV color space is proposed in order to achieve effective compression. Linear quantization of compressed image data is introduced in order to improve the compression rate. Through experiments using 100 typical images (Corel Gallery, Arizona Directory), PSNR increases at 7.9-14.1% compared with the conventional method under the condition that compression rates are 0.0234-0.0938.

scholarly journals A hybrid predictive technique for lossless image compression

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
N. A. N. Azman ◽  
Samura Ali ◽  
Rozeha A. Rashid ◽  
Faiz Asraf Saparudin ◽  
Mohd Adib Sarijari
Keyword(s):  
Image Compression ◽  
Data Storage ◽  
Image Data ◽  
Differential Pulse ◽  
Image Size ◽  
Pulse Code Modulation ◽  
Huge Amount ◽  
Transmission Bandwidth ◽  
Lossless Image Compression ◽  
Differential Pulse Code Modulation

Compression of images is of great interest in applications where efficiency with respect to data storage or transmission bandwidth is sought.The rapid growth of social media and digital networks have given rise to huge amount of image data being accessed and exchanged daily. However, the larger the image size, the longer it takes to transmit and archive. In other words, high quality images require huge amount of transmission bandwidth and storage space. Suitable image compression can help in reducing the image size and improving transmission speed. Lossless image compression is especially crucial in fields such as remote sensing healthcare network, security and military applications as the quality of images needs to be maintained to avoid any errors during analysis or diagnosis. In this paper, a hybrid prediction lossless image compression algorithm is proposed to address these issues. The algorithm is achieved by combining predictive Differential Pulse Code Modulation (DPCM) and Integer Wavelet Transform (IWT). Entropy and compression ratio calculation are used to analyze the performance of the designed coding. The analysis shows that the best hybrid predictive algorithm is the sequence of DPCM-IWT-Huffman which has bits sizes reduced by 36%, 48%, 34% and 13% for tested images of Lena, Cameraman, Pepper and Baboon, respectively.

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