JPG, PNG and BMP image compression using discrete cosine transform

Abstract

This paper proposes image compression using discrete cosine transform (DCT) for the format of joint photographic expert groups (JPEG) or JPG, portable network graphic (PNG) and bitmap (BMP). These three extensions are the most popular types used in current image processing storage. The purpose of image compression is to produce lower memory usage or to reduce memory file. This process removes redundant information of each pixel. The challenge for image compression process is to maintain the quality of images after the compression process. Hence, this article utilizes the DCT technique to sustain the image quality and at the same time reduces the image storage size. The effectiveness of the DCT technique has been reasonable over some real images and the implementation of the technique has been compared with different types of image extensions. Matlab software is an important platform for this project in order to write a program and perform the progress of project phase by phase to achieve the expected results. Based on the tested images, the DCT technique in image compression is capable to reduce the image storage memory in average about 50% of each image tested.

Multimode system condition monitoring using sparsity reconstruction for quality control

“…Its principle is to calculate the ratio between SPEj and the faulty SPE, after calculating the SPE statistic of the same reconstructed variable [24], it is defined as (12),…”

“…These methods were developed in the 1980s but were quickly abandoned because they were considered unpromising. With the improvement of computing power, new and richer databases [11], [12], and great advances in optimization techniques [13], [14], deep learning has recently reached exceptional performances for different tasks [15]- [17]. Then, we focus on the stacked sparse autoencoder (SSAE) [18], which finds a hidden representation in massive data by using a multi-layer encoder-decoder structure, where small abstract features are extracted to reconstruct the input data.…”

Flow incorporated neural network based lightweight video compression architecture

“…neural networks [8], [11], [21]. These networks were optimized by minimizing the mean square error between the real and regenerated frames while the number of bits used being ignored.…”

“…The initial techniques employed non-adaptive arithmetic coding but the advanced compression networks, proposed later, comprises of rate distortion optimization schemes and adaptive coding to enhance the compression efficiency and the performance. An efficient discrete cosine based image compression technique for JPG, PNG and BMP formats have also been proposed and experimented [21]. Several prominent works were also done in the field of color image compression [22], [23].…”