Deep Pipeline Architecture for Fast Fractal Color Image Compression Utilizing Inter-Color Correlation

Fractal compression technique is a well-known technique that encodes an image by mapping the image into itself and this requires performing a massive and repetitive search. Thus, the encoding time is too long, which is the main problem of the fractal algorithm. To reduce the encoding time, several hardware implementations have been developed. However, they are generally developed for grayscale images, and using them to encode colour images leads to doubling the encoding time 3× at least. Therefore, in this paper, new high-speed hardware architecture is proposed for encoding RGB images in a short time. Unlike the conventional approach of encoding the colour components similarly and individually as a grayscale image, the proposed method encodes two of the colour components by mapping them directly to the most correlated component with a searchless encoding scheme, while the third component is encoded with a search-based scheme. This results in reducing the encoding time and also in increasing the compression rate. The parallel and deep-pipelining approaches have been utilized to improve the processing time significantly. Furthermore, to reduce the memory access to the half, the image is partitioned in such a way that half of the matching operations utilize the same data fetched for processing the other half of the matching operations. Consequently, the proposed architecture can encode a 1024×1024 RGB image within a minimal time of 12.2 ms, and a compression ratio of 46.5. Accordingly, the proposed architecture is further superior to the state-of-the-art architectures.©2022 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

New Technique For Reducing Symmetry Mapping In Colored FIC Based on Moments Features

This paper studded the effect of symmetry mapping process on thecompression parameters of the fractal color image compression by momentfeatures was studded. Feature of moment utilized to reduction the symmetry mapping from 8 to only one. The operation of reduction is achieved by using predictor to symmetry mappings; the predictor will predict specific symmetry mapping according a specific feature of moments to one of eight. Such that eight versions (blocks) are produced for each domain block, so this case needs 8 mappings and it requires more computational time. Our suggestion will directly reduce the encoding time 1:8 times

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression

Novel Color Image Compression Algorithm Based-On Quadtree

This paper presents a novel algorithm having two image processing systems that have the ability to compress the colour image. The proposed systems divides the colour image into RGB components, each component is selected to be divided. The division processes of the component into blocks are based on quad tree method. For each selection, the other two components are divided using the same blocks coordinates of the selected divided component. In the first system, every block has three minimum values and three difference values. While the other system, every block has three minimum values and one average difference. From experiments, it is found that the division according to the G component is the best giving good visual quality of the compressed images with appropriate compression ratios. It is also noticed, the performance of the second system is better than the first one. The obtained compression ratios ofthe second system are between 1.3379 and 5.0495 at threshold value 0.1, and between 2.3476 and 8.9713 at threshold value 0.2

The Use of Quadtree Range Domain Partitioning with Fast Double Moment Descriptors to Enhance FIC of Colored Image

In this paper, an enhanced fractal image compression system (FIC) is proposed; it is based on using both symmetry prediction and blocks indexing to speed up the blocks matching process. The proposed FIC uses quad tree as variable range block partitioning mechanism. two criteria’s for guiding the partitioning decision are used: The first one uses sobel-based edge magnitude, whereas the second uses the contrast of block. A new set of moment descriptors are introduced, they differ from the previously used descriptors by their ability to emphasize the weights of different parts of each block. The effectiveness of all possible combinations of double moments descriptors has been investigated. Furthermore, a fast computation mechanism is introduced to compute the moments attended to improve the overall computation cost. the results of applied tests on the system for the cases “variable and fixed range” block partitioning mechanism indicated that the variable partitioning scheme can produce better results than fixed partitioning one (that is, 4 × 4 block) in term of compression ratio, faster than and PSNR does not significantly decreased

Transformation and dynamic visualization of images from computer through an FPGA in a matrix of LED

This article shows the implementation of a system that uses a graphic interface to load a digital image into a programmable logic device, which is stored in its internal RAM memory and is responsible for visualizing it in a matrix of RGB LEDs, so that This way, the LEDs show an equivalent to the image that was sent from the PC, conserving an aspect ratio and respecting as much as possible the color of the original image. To carry out this task, a Matlab script was designed to load the image, convert and format the data, which are transmitted to the FPGA using the RS232 protocol. The FPGA is in charge of receiving them, storing them and generating all the signals of control and synchronization of the system including the control of the PWM signals necessary to conserve the brightness of each one of the LEDs. This system allows the visualization of static images in standard formats and, in addition, thanks to the flexibility of the hardware used, it allows the visualization of moving images type GIF

Enhancement of student performance prediction using modified K-nearest neighbor

The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically

Quadtree partitioning scheme of color image based

Image segmentation is an essential complementary process in digital image processing and computer vision, but mostly utilizes simple segmentation techniques, such as fixed partitioning scheme and global thresholding techniques due to their simplicity and popularity, in spite of their inefficiency. This paper introduces a new split-merge segmentation process for a quadtree scheme of colour images, based on exploiting the spatial and spectral information embedded within the bands and between bands, respectively. The results show that this technique is efficient in terms of quality of segmentation and time, which can be used in standard techniques as alternative to a fixed partitioning scheme

Subband Fractal Coding of Color Images using Correlations between Y and C Components

This paper proposes a new efficient fractal coding scheme for color images and demonstrates its simulation experimental results. The proposed scheme utilizes the correlation between a luminance component (Y) and two color difference components (C_b, C_r) in the fractal coding. It also employs subband decomposition and vector quantization. Each input component (Y, C_r, or C_b) is first divided into 7 subband images. The fractal block coding for Y, Cr and C_b is carried out only for the lowest frequency subband images. For the other higher frequency subband images, vector quantization (VQ) is carried out. The fractal coded data of Y, except for the range block average pixel values, are applied to fractal coding of Cr and C_b. Therefore only the range block average pixel values are coded for C_r and C_b. In order to enhance the coding performance further more, the residual differences between the original and reconstructed low frequency subband images are quantized and entropy coded. Computer simulation experiments were carried out in order to evaluate the proposed coding scheme. The results show that the proposed scheme gives better performance compared to the conventional fractal block coding scheme which encodes Y, C_r and C_b independently. The proposed scheme also outperforms the JPEG coding scheme