A comparison of select image-compression algorithms for an electronic still camera

This effort is a study of image-compression algorithms for an electronic still camera. An electronic still camera can record and transmit high-quality images without the use of film, because images are stored digitally in computer memory. However, high-resolution images contain an enormous amount of information, and will strain the camera's data-storage system. Image compression will allow more images to be stored in the camera's memory. For the electronic still camera, a compression algorithm that produces a reconstructed image of high fidelity is most important. Efficiency of the algorithm is the second priority. High fidelity and efficiency are more important than a high compression ratio. Several algorithms were chosen for this study and judged on fidelity, efficiency and compression ratio. The transform method appears to be the best choice. At present, the method is compressing images to a ratio of 5.3:1 and producing high-fidelity reconstructed images

Image Compression System using ANN

The rapid growth of digital imaging applications, including desktop publishing, multimedia, teleconferencing, and high definition television (HDTV) has increased the need for effective and standardized image compression techniques. Among the emerging standards are JPEG, for compression of still images; MPEG, for compression of motion video; and CCITT H.261 (also known as Px64), for compression of video telephony and teleconferencing. All three of these standards employ a basic technique known as the discrete cosine transform (DCT), Developed by Ahmed, Natarajan, and Rao [1974]. Image compression using Discrete Cosine Transform (DCT) is one of the simplest commonly used compression methods. The quality of compressed images, however, is marginally reduced at higher compression ratios due to the lossy nature of DCT compression, thus, the need for finding an optimum DCT compression ratio. An ideal image compression system must yield high quality compressed images with good compression ratio, while maintaining minimum time cost. The neural network associates the image intensity with its compression ratios in search for an optimum ratio

Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients

An efficient color image compression technique

We present a new image compression method to improve visual perception of the decompressed images and achieve higher image compression ratio. This method balances between the compression rate and image quality by compressing the essential parts of the image-edges. The key subject/edge is of more significance than background/non-edge image. Taking into consideration the value of image components and the effect of smoothness in image compression, this method classifies the image components as edge or non-edge. Low-quality lossy compression is applied to non-edge components whereas high-quality lossy compression is applied to edge components. Outcomes show that our suggested method is efficient in terms of compression ratio, bits per-pixel and peak signal to noise ratio

Toward an image compression algorithm for the high-resolution electronic still camera

Taking pictures with a camera that uses a digital recording medium instead of film has the advantage of recording and transmitting images without the use of a darkroom or a courier. However, high-resolution images contain an enormous amount of information and strain data-storage systems. Image compression will allow multiple images to be stored in the High-Resolution Electronic Still Camera. The camera is under development at Johnson Space Center. Fidelity of the reproduced image and compression speed are of tantamount importance. Lossless compression algorithms are fast and faithfully reproduce the image, but their compression ratios will be unacceptably low due to noise in the front end of the camera. Future efforts will include exploring methods that will reduce the noise in the image and increase the compression ratio

Transformasi Geometri Rotasi Citra Digital Untuk Mendapatkan Kompresi Optimal Menggunakan Metode Lossless Dan Lossy

Data has a fairly important meaning, because it contains the information needed. Besides being important, the process of data exchange and storage certainly requires space and requires high costs. For this reason, data compression is an interesting topic for continuous research. The success of previous research in obtaining optimal compression, it will be more interesting, if tested on the Lossless and Lossy Compression Methods, which of the two methods results in more optimal compression. The amount of the compression ratio without the image rotation process. For regular images obtained Huffman Compression Ratio 48.7076% and Wavelet Compression 47.3888%. If we compare the compression ratio by comparing the initial file size without playback compared with the final file size after 90 degrees of playback, the ratio is 61.03%. Meanwhile, for irregular images, a ratio of 49.79% was obtained at 180 degrees of image rotation. The results of compression, both without and with rotation, Wavelet Compression is more optimal than Huffman Compression. Keywords: Digital Image, Test Image, Lossless, Lossy, Huffmann Algorithm, Haar Wavele

A Comparison of JPEG and Wavelet Compression Applied to CT Images

A study of image compression is becoming more important since an uncompressed image requires a large amount of storage space and high transmission bandwidth. This paper focuses on the quantitative comparison of lossy compression methods applied to a variety of 8-bit Computed Tomography (CT) images. Joint Photographic Experts Group UPEG) and Wavelet compression algorithms were used on a set of CT images, namely brain, chest, and abdomen. These algorithms were applied to each image to achieve maximum compression ratio (CR). Each compressed image was then decompressed and quantitative analysis was performed to compare each compressed-then-decompressed image with its corresponding original image. The Wavelet Compression Engine (standard edition 2.5), and ]pEG Wizard (Version 1.1.7) were used in this study. The statistical indices computed were mean square error (MSE) , signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR). Our results show that Wavelet compression yields better compression quality compared with ]pEG for higher compression. From the numerical values obtained we observe that the PSNR for chest and abdomen images is equal to 24 dB for compression ratio up to 31:1 by using ]pEG and 18 dB for compression ratio up to 33:1 by using wavelet. For brain image the PSNR is equal to 26 to 30 dB for compression ratio between 40 to 125:1 by using ]pEG, whereas by using wavelet the PSNR is equal to 22 to 34 dB for compression ratio between 52 to 240:1. The degree of compression was also found dependent on the anatomic structure and the complexity of the CT images

Survey of Hybrid Image Compression Techniques

A compression process is to reduce or compress the size of data while maintaining the quality of information contained therein. This paper presents a survey of research papers discussing improvement of various hybrid compression techniques during the last decade. A hybrid compression technique is a technique combining excellent properties of each group of methods as is performed in JPEG compression method. This technique combines lossy and lossless compression method to obtain a high-quality compression ratio while maintaining the quality of the reconstructed image. Lossy compression technique produces a relatively high compression ratio, whereas lossless compression brings about high-quality data reconstruction as the data can later be decompressed with the same results as before the compression. Discussions of the knowledge of and issues about the ongoing hybrid compression technique development indicate the possibility of conducting further researches to improve the performance of image compression method