Effect of Channel Consideration on Auto Encoders for Color Image Compression using Deep Learning

In this paper, we have studied the effect of channels consideration on autoencoders for color image compression. The study is made in relation to RGB patch in an image and individual channel patches to know the effectiveness of what criteria is to be used while processing the image for compression. The study reveals that the RGB patch consideration in a color image is better than considering the channels individually. The chaotic (or scramble) image is given as input to autoencoder for compression and this helps to overcome the threat by the intruder and as well protection to data transmitted.

Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm

Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.

Color image compression based on spatial and magnitude signal decomposition

<p>In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.</p>

Testing of Image Compression Methods

Seven console codecs BMF 2.01 (BMF.exe), WebP (cwebp.exe, dwebp.exe), PAQ8L (paq-8l_intel.exe), PAQ8P (paq8p_sse2.exe), JPEG-LS (jpeg.exe), JPEG2000 (convert.exe), PNG (opting.exe) are tested on a set of 19 grayscale images and 10 color images (widespread images used in testing compression methods). An image compression program tester was developed. The tester receives images and executable files of image compression programs, and programs are started for each input image to compress and restore the image. The program operation results are contained in an HTML/CSS file, which includes, among other information, the bitrates achieved by the compression programs and the results of checking how successfully the compressed files were restored. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods have been made to compare the effectiveness of the multipredictors that lie at the heart of the Blend-A13+, Blend-16, Blend-20 methods. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods consist of multipredictors used in the Blend-A13+, Blend-16, Blend-20, methods, and 13 and 16 elementary predictors used in the Blend-A13+ and Blend-16 methods, respectively. The predictor GAP+ is replaced by the predictor GAP; the modeling like JPEG-LS is replaced by contextual modeling with quantization of a context from differences of pixels from the vicinity of the coded pixel, the arithmetic coder, and the reversible intercolor RGB-YUV transformation from JPEG2000. For many images, the obtained partial clones outperformed the results yielded by the JPEG-LS, JPEG2000, PNG methods and gave results at the level provided by the PAQ8L and WebP compression methods. In general, the BMF2.01 method demonstrated the best results on the test set of images. On the test set of images, the multipredictors from Blend-16 and Blend-20 unexpectedly provided color image compression results poorer than the multipredictor from the Blend-A13+ method. In compressing grayscale images, the multipredictor from Blend-20 yielded better results than the multipredictors from Blend-16, Blend-A13+.