KOMPRESI CITRA MENGGUNAKAN FRAKTAL DENGAN PENERAPAN SPIRAL ARCHITECTURE

ABSTRAKSI: Representasi citra digital membutuhkan media penyimpanan yang besar. . Akan tetapi, saat ini kebanyakan aplikasi memerlukan representasi citra dengan kebutuhan media penyimpanan sesedikit mungkin. Kompresi citra bertujuan meminimalkan kebutuhan media penyimpanan untuk merepresentasikan citra digital.Pada tugas akhir ini dikembangkan suatu metode penerapan spiral architecture dalam kompresi citra menggunakan fraktal. Spiral architecture adalah teknik yang menggunakan arsitektur heksagonal dalam representasi citra. Untuk mengetahui performansi hasil proses kompresi dilakukan melalui perhitungan PSNR, rasio kompresi dan waktu kompresi-dekompresi.Berdasarkan seluruh hasil pengujian, sistem kompresi citra menggunakan metode penerapan spiral architecture dalam kompresi citra menggunakan fraktal memiliki waktu kompresi dan rasio kompresi yang lebih baik dibandingkan dengan sistem kompresi citra fraktal dalam arsitektur biasa. Rasio kompresi dengan rata-rata sebesar 91,92 % & 90,76% dan waktu kompresi-dekompresi dengan rata-rata 172,83 s & 204,36s. Sebaliknya kompresi citra dengan penerapan spiral architecture lebih baik dari sisi PSNR yaitu dengan PSNR rata-rata 23,09 dB dibanding kompresi citra fraktal tanpa penerapan spiral architecture sebesar 20,73 dB.Kata Kunci : Kompresi Citra, Spiral Architecture, Kompresi Citra FraktalABSTRACT: Image representation needs large memory. Yet most application needs less memory for image representation. Image compression’s goal is to minimize the need of memory to represent digital image.In this final project, the author develop a simulation of fractal image compression using spiral architecture. Spiral architecture is a technique using hexagonal architecture to represent an image. The performance is count by PSNR, compression ratio and time for compression-decompression.Based on the the simulation result, fractal image compression using spiral architecture has compression time and compression ratio better than fractal image compression. The average compression ratio is 91,92% and 90,76%, then the average time for compression-decompression 172,83 s and 204,36s. In the other hand, fractal image compression using spiral architecture has PSNR better than fractal image compression with the average PSNR is 23,09 dB and 20,73 dB.Keyword: Image Compression, Spiral Architecture, Fractal Image Compressio

A Fast Fractal Image Compression Algorithm Combined with Graphic Processor Unit

Directed against the characteristics of computational intensity of fractal image compression encoding, a serial-parallel transfer mechanism is built for encoding procedures. By utilizing the properties of single instruction and multithreading execution of compute unified device architecture (CUDA), the parallel computational model of fractal encoding is built on the graphic processor unit(GPU) in order to parallelize the considerably time-consuming serial execution process of searching for the block of best match. The experimental result indicates, the algorithm in this paper shortens the encoding time to the millisecond scale and significantly boosts the execution efficiency of fractal image encoding algorithm while keeping the decoded image in good quality

A survey of parallel algorithms for fractal image compression

This paper presents a short survey of the key research work that has been undertaken in the application of parallel algorithms for Fractal image compression. The interest in fractal image compression techniques stems from their ability to achieve high compression ratios whilst maintaining a very high quality in the reconstructed image. The main drawback of this compression method is the very high computational cost that is associated with the encoding phase. Consequently, there has been significant interest in exploiting parallel computing architectures in order to speed up this phase, whilst still maintaining the advantageous features of the approach. This paper presents a brief introduction to fractal image compression, including the iterated function system theory upon which it is based, and then reviews the different techniques that have been, and can be, applied in order to parallelize the compression algorithm

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

Sparsely Aggregated Convolutional Networks

We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers. Such aggregation is critical to facilitate training of very deep networks in an end-to-end manner. This is a primary reason for the widespread adoption of residual networks, which aggregate outputs via cumulative summation. While subsequent works investigate alternative aggregation operations (e.g. concatenation), we focus on an orthogonal question: which outputs to aggregate at a particular point in the network. We propose a new internal connection structure which aggregates only a sparse set of previous outputs at any given depth. Our experiments demonstrate this simple design change offers superior performance with fewer parameters and lower computational requirements. Moreover, we show that sparse aggregation allows networks to scale more robustly to 1000+ layers, thereby opening future avenues for training long-running visual processes.Comment: Accepted to ECCV 201