COMPARATIVE ANALYSIS OF COMPACT METHODS REPRESENTATIONS OF GRAPHIC INFORMATION

The main characteristics of graphic information compression methods with losses and without losses (RLE, LZW, Huffman's method, DEFLATE, JBIG, JPEG, JPEG 2000, Lossless JPEG, fractal and Wawelet) are analyzed in the article. Effective transmission and storage of images in railway communication systems is an important task now. Because large images require large storage resources. This task has become very important in recent years, as the problems of information transmission by telecommunication channels of the transport infrastructure have become urgent. There is also a great need for video conferencing, where the task is to effectively compress video data - because the greater the amount of data, the greater the cost of transmitting information, respectively. Therefore, the use of image compression methods that reduce the file size is the solution to this task. The study highlights the advantages and disadvantages of compression methods. The comparative analysis the basic possibilities of compression methods of graphic information is carried out. The relevance lies in the efficient transfer and storage of graphical information, as big data requires large resources for storage. The practical significance lies in solving the problem of effectively reducing the data size by applying known compression methods.

Wavelet-based image compression using mathematical morphology and self organization feature map

Image compression using transform coding technique has been widely used in practice. However, wavelet transform is the only method that provides both spatial and frequency domain information. These properties of wavelet transform greatly help in identification and selection of significant and non-significant coefficients from amongst the wavelet coefficients. Wavelet transform based image compression result in an improved compression ratio as well as image quality and thus both the signficant coefficients and their positions within an image are encoded and transmitted. In this thesis a wavelet based image compression system is presented that uses mathematical morphology and self organizing feature map (MMSOFM). The significance map is preprocessed using mathematical morphology operators to identify and creat clusters of significant coefficients. A self-organizing feature map (SOFM) is then used to encode the significance map. Experimental results are shown and comparisons with JPEG and JPEG 2000 are made to emphasize the results of this compression system.

Wavelet-based image compression using mathematical morphology and self organization feature map

Image compression using transform coding technique has been widely used in practice. However, wavelet transform is the only method that provides both spatial and frequency domain information. These properties of wavelet transform greatly help in identification and selection of significant and non-significant coefficients from amongst the wavelet coefficients. Wavelet transform based image compression result in an improved compression ratio as well as image quality and thus both the signficant coefficients and their positions within an image are encoded and transmitted. In this thesis a wavelet based image compression system is presented that uses mathematical morphology and self organizing feature map (MMSOFM). The significance map is preprocessed using mathematical morphology operators to identify and creat clusters of significant coefficients. A self-organizing feature map (SOFM) is then used to encode the significance map. Experimental results are shown and comparisons with JPEG and JPEG 2000 are made to emphasize the results of this compression system.

Multiresolution analysis for classification and compression

This thesis contains the application of the DWT [Discrete Wavelet Transform] for classification and compression of biomedical images (mammograms, small bowel and retinal). The shift-invariant DWT and several textural descriptors were used to provide scale, translation and semi-rotational (RST) invariant features. The features were classified using LDA with the leave one out method to combat small database sizes. The small bowel images achieved a classification rate of 75% and is the first reported work in the area, the retinal images achieved 81% classification rate and the mammograms achieved a rate of 59%. The success of the system is a [sic] due to the RST-invariant features which accounted for various sized masses, different camera angles and textural differences between pathologies. Any failures are a result of overlapping tissues which masked the pathologies. JPEG 2000 was the wavelet-based compressor used and it was compared to JPEG-LS, LJPEG, adaptive Huffman, arithmetic and LZW codes. For 12bpp mammgrams, JPEG 2000 offered the best compression (CR of 9.319 and R of 1.288bpp), but suffered from slow compression speeds (501.3 Ksymbols/s). Compression was investigated solely for mammograms since they were the only images stored in raw formats.

Multiresolution analysis for classification and compression

This thesis contains the application of the DWT [Discrete Wavelet Transform] for classification and compression of biomedical images (mammograms, small bowel and retinal). The shift-invariant DWT and several textural descriptors were used to provide scale, translation and semi-rotational (RST) invariant features. The features were classified using LDA with the leave one out method to combat small database sizes. The small bowel images achieved a classification rate of 75% and is the first reported work in the area, the retinal images achieved 81% classification rate and the mammograms achieved a rate of 59%. The success of the system is a [sic] due to the RST-invariant features which accounted for various sized masses, different camera angles and textural differences between pathologies. Any failures are a result of overlapping tissues which masked the pathologies. JPEG 2000 was the wavelet-based compressor used and it was compared to JPEG-LS, LJPEG, adaptive Huffman, arithmetic and LZW codes. For 12bpp mammgrams, JPEG 2000 offered the best compression (CR of 9.319 and R of 1.288bpp), but suffered from slow compression speeds (501.3 Ksymbols/s). Compression was investigated solely for mammograms since they were the only images stored in raw formats.

Coverless Steganography Based on Motion Analysis of Video

With the rapid development of interactive multimedia services and camera sensor networks, the number of network videos is exploding, which has formed a natural carrier library for steganography. In this study, a coverless steganography scheme based on motion analysis of video is proposed. For every video in the database, the robust histograms of oriented optical flow (RHOOF) are obtained, and the index database is constructed. The hidden information bits are mapped to the hash sequences of RHOOF, and the corresponding indexes are sent by the sender. At the receiver, through calculating hash sequences of RHOOF from the cover video, the secret information can be extracted successfully. During the whole process, the cover video remains original without any modification and has a strong ability to resist steganalysis. The capacity is investigated and shows good improvement. The robustness performance is prominent against most attacks such as pepper and salt noise, speckle noise, MPEG-4 compression, and motion JPEG 2000 compression. Compared with the existing coverless information hiding schemes based on images, the proposed method not only obtains a good trade-off between hiding information capacity and robustness but also can achieve higher hiding success rate and lower transmission data load, which shows good practicability and feasibility.