80 research outputs found

    Transmission of compressed images over power line channel

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    In the telecommunications industry, the use of existing power lines has drawn the attention of many researchers in the recent years. PLC suffers from impulsive noise that can affect data transmission by causing bit or burst errors. In this thesis, PLC channel was used as a transmission scheme to transmit compressed still images using FFT-OFDM. When lossy compression is applied to an image, a small loss of quality in the compressed image is tolerated. One of the challenging tasks in image compression and transmission is the trade-off between compression ratio and image quality. Therefore, we utilized the latest developments in quality assessment techniques, SSIM, to adaptively optimize this trade-off to the type of image application which the compression is being used for. A comparison between different compression techniques, namely, discrete cosine transform (DCT), discrete wavelet transform (DWT), and block truncation coding (BTC) was carried out. The performance criteria for our compression methods include the compression ratio, relative root-meansquared (RMS) error of the received data, and image quality evaluation via structural similarity index (SSIM). Every link in a powerline has its own attenuation profile depending on the length, layout, and cable types. Also, the influences of multipath fading due to reflections at branching point vary the attenuation profile of the link. As a result, we observed the effect of different parameters of the PLC channel based on the number of paths, and length of link on the quality of the image. Simulations showed that the image quality is highly affected by the interaction of the distance of PLC channel link and the number of multipath reflections. The PLC channel is assumed to be subjected to Gaussian and impulsive noises. There are two types of impulsive noise: asynchronous impulsive noise and periodic impulsive noise synchronous to the mains frequency. BER analysis was performed to compare the performance of the channel for the two types of impulsive noise under three impulsive scenarios. The first scenario is named as "heavily disturbed" and it was measured during the evening hours in a transformer substation in an industrial area. The second scenario is named as "moderately disturbed" and was recorded in a transformer substation in a residential area with detached and terraced houses. The third scenario is named as "weakly disturbed" and was recorded during night-time in an apartment located in a large building. The experiments conducted showed that both types of noise performed similarly in the three impulsive noise scenarios. We implemented Bose-Chaudhuri-Hocquenghen (BCH) coding to study the performance of Power Line Channel (PLC) impaired by impulsive noise and AWGN. BCH codes and RS codes are related and their decoding algorithms are quite similar. A comparison was made between un-coded system and BCH coding system. The performance of the system is assessed by the quality of the image for different sizes of BCH encoder, in three different impulsive environments. Simulation results showed that with BCH coding, the performance of the PLC system has improved dramatically in all three impulsive scenarios

    Entropy in Image Analysis II

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    Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

    Consolidating Literature for Images Compression and Its Techniques

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    With the proliferation of readily available image content, image compression has become a topic of considerable importance. As, rapidly increase of digital imaging demand, storage capability aspect should be considered. Therefore, image compression refers to reducing the size of image for minimizing storage without harming the image quality. Thus, an appropriate technique is needed for image compression for saving capacity as well as not losing valuable information. This paper consolidates literature whose characteristics have focused on image compression, thresholding algorithms, quantization algorithms. Later, related research on these areas are presented

    Data hiding techniques in steganography using fibonacci sequence and knight tour algorithm

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    The foremost priority in the information and communication technology era, is achieving an efficient and accurate steganography system for hiding information. The developed system of hiding the secret message must capable of not giving any clue to the adversaries about the hidden data. In this regard, enhancing the security and capacity by maintaining the Peak Signal-to-Noise Ratio (PSNR) of the steganography system is the main issue to be addressed. This study proposed an improved for embedding secret message into an image. This newly developed method is demonstrated to increase the security and capacity to resolve the existing problems. A binary text image is used to represent the secret message instead of normal text. Three stages implementations are used to select the pixel before random embedding to select block of (64 ร— 64) pixels, follows by the Knight Tour algorithm to select sub-block of (8 ร— 8) pixels, and finally by the random pixels selection. For secret embedding, Fibonacci sequence is implemented to decomposition pixel from 8 bitplane to 12 bitplane. The proposed method is distributed over the entire image to maintain high level of security against any kind of attack. Gray images from the standard dataset (USC-SIPI) including Lena, Peppers, Baboon, and Cameraman are implemented for benchmarking. The results show good PSNR value with high capacity and these findings verified the worthiness of the proposed method. High complexities of pixels distribution and replacement of bits will ensure better security and robust imperceptibility compared to the existing systems in the literature

    Digital image compression

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    Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

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    This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

    ๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜๋ฅผ ์œ„ํ•œ ๊ณ ์ • ๋น„์œจ ์••์ถ• ํ•˜๋“œ์›จ์–ด ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2016. 2. ์ดํ˜์žฌ.๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜์—์„œ์˜ ์••์ถ• ๋ฐฉ์‹์€ ์ผ๋ฐ˜์ ์ธ ๋น„๋””์˜ค ์••์ถ• ํ‘œ์ค€๊ณผ๋Š” ๋‹ค๋ฅธ ๋ช‡ ๊ฐ€์ง€ ํŠน์ง•์ด ์žˆ๋‹ค. ์ฒซ์งธ, ํŠน์ˆ˜ํ•œ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋‘˜์งธ, ์••์ถ• ์ด๋“, ์†Œ๋น„ ์ „๋ ฅ, ์‹ค์‹œ๊ฐ„ ์ฒ˜๋ฆฌ ๋“ฑ์„ ์œ„ํ•ด ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ๊ฐ€ ์ž‘๊ณ , ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์••์ถ•๋ฅ ์ด ๋‚ฎ๋‹ค. ์…‹์งธ, ๋ž˜์Šคํ„ฐ ์ฃผ์‚ฌ ์ˆœ์„œ์— ์ ํ•ฉํ•ด์•ผ ํ•œ๋‹ค. ๋„ท์งธ, ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ํฌ๊ธฐ๋ฅผ ์ œํ•œ์‹œํ‚ค๊ฑฐ๋‚˜ ์ž„์˜ ์ ‘๊ทผ์„ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์••์ถ• ๋‹จ์œ„๋‹น ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ •ํ™•ํžˆ ๋งž์ถœ ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด์™€ ๊ฐ™์€ ํŠน์ง•์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ์„ธ ๊ฐ€์ง€ ์••์ถ• ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. LCD ์˜ค๋ฒ„๋“œ๋ผ์ด๋ธŒ๋ฅผ ์œ„ํ•œ ์••์ถ• ๋ฐฉ์‹์œผ๋กœ๋Š” BTC(block truncation coding) ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์••์ถ• ์ด๋“์„ ์ฆ๊ฐ€์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ๋ชฉํ‘œ ์••์ถ•๋ฅ  12์— ๋Œ€ํ•œ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋Š”๋ฐ, ์••์ถ• ํšจ์œจ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์ด์›ƒํ•˜๋Š” ๋ธ”๋ก๊ณผ์˜ ๊ณต๊ฐ„์  ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜์—ฌ ๋น„ํŠธ๋ฅผ ์ ˆ์•ฝํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‘ ๋ฒˆ์งธ๋Š” ๋‹จ์ˆœํ•œ ์˜์—ญ์€ 2ร—16 ์ฝ”๋”ฉ ๋ธ”๋ก, ๋ณต์žกํ•œ ์˜์—ญ์€ 2ร—8 ์ฝ”๋”ฉ ๋ธ”๋ก์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. 2ร—8 ์ฝ”๋”ฉ ๋ธ”๋ก์„ ์ด์šฉํ•˜๋Š” ๊ฒฝ์šฐ ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ๋งž์ถ”๊ธฐ ์œ„ํ•˜์—ฌ ์ฒซ ๋ฒˆ์งธ ๋ฐฉ๋ฒ•์œผ๋กœ ์ ˆ์•ฝ๋œ ๋น„ํŠธ๋ฅผ ์ด์šฉํ•œ๋‹ค. ์ €๋น„์šฉ ๊ทผ์ ‘-๋ฌด์†์‹ค ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์••์ถ•์„ ์œ„ํ•œ ๋ฐฉ์‹์œผ๋กœ๋Š” 1D SPIHT(set partitioning in hierarchical trees) ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. SPIHT์€ ๊ณ ์ • ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ๋งž์ถ”๋Š”๋ฐ ๋งค์šฐ ํšจ๊ณผ์ ์ธ ์••์ถ• ๋ฐฉ์‹์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 1D ํ˜•ํƒœ์ธ 1D SPIHT์€ ๋ž˜์Šคํ„ฐ ์ฃผ์‚ฌ ์ˆœ์„œ์— ์ ํ•ฉํ•จ์—๋„ ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ง„ํ–‰๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ 1D SPIHT์˜ ๊ฐ€์žฅ ํฐ ๋ฌธ์ œ์ ์ธ ์†๋„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด 1D SPIHT ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ณ‘๋ ฌ์„ฑ์„ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ˜•ํƒœ๋กœ ์ˆ˜์ •๋œ๋‹ค. ์ธ์ฝ”๋”์˜ ๊ฒฝ์šฐ ๋ณ‘๋ ฌ ์ฒ˜๋ฆฌ๋ฅผ ๋ฐฉํ•ดํ•˜๋Š” ์˜์กด ๊ด€๊ณ„๊ฐ€ ํ•ด๊ฒฐ๋˜๊ณ , ํŒŒ์ดํ”„๋ผ์ธ ์Šค์ผ€์ฅด๋ง์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋œ๋‹ค. ๋””์ฝ”๋”์˜ ๊ฒฝ์šฐ ๋ณ‘๋ ฌ๋กœ ๋™์ž‘ํ•˜๋Š” ๊ฐ ํŒจ์Šค๊ฐ€ ๋””์ฝ”๋”ฉํ•  ๋น„ํŠธ์ŠคํŠธ๋ฆผ์˜ ๊ธธ์ด๋ฅผ ๋ฏธ๋ฆฌ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋„๋ก ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ˆ˜์ •๋œ๋‹ค. ๊ณ ์ถฉ์‹ค๋„(high-fidelity) RGBW ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ ๋ฐฉ์‹์œผ๋กœ๋Š” ์˜ˆ์ธก ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ์ œ์•ˆ ์˜ˆ์ธก ๋ฐฉ์‹์€ ๋‘ ๋‹จ๊ณ„์˜ ์ฐจ๋ถ„ ๊ณผ์ •์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ๊ณต๊ฐ„์  ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜๋Š” ๋‹จ๊ณ„์ด๊ณ , ๋‘ ๋ฒˆ์งธ๋Š” ์ธํ„ฐ-์ปฌ๋Ÿฌ ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜๋Š” ๋‹จ๊ณ„์ด๋‹ค. ์ฝ”๋”ฉ์˜ ๊ฒฝ์šฐ ์••์ถ• ํšจ์œจ์ด ๋†’์€ VLC(variable length coding) ๋ฐฉ์‹์„ ์ด์šฉํ•˜๋„๋ก ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ VLC ๋ฐฉ์‹์€ ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ์ •ํ™•ํžˆ ๋งž์ถ”๋Š”๋ฐ ์–ด๋ ค์›€์ด ์žˆ์—ˆ์œผ๋ฏ€๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Golomb-Rice ์ฝ”๋”ฉ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๊ณ ์ • ๊ธธ์ด ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ์ œ์•ˆ ์ธ์ฝ”๋”๋Š” ํ”„๋ฆฌ-์ฝ”๋”์™€ ํฌ์Šคํ„ฐ-์ฝ”๋”๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ํ”„๋ฆฌ-์ฝ”๋”๋Š” ํŠน์ • ์ƒํ™ฉ์— ๋Œ€ํ•˜์—ฌ ์‹ค์ œ ์ธ์ฝ”๋”ฉ์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ๋‹ค๋ฅธ ๋ชจ๋“  ์ƒํ™ฉ์— ๋Œ€ํ•œ ์˜ˆ์ธก ์ธ์ฝ”๋”ฉ ์ •๋ณด๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ํฌ์Šคํ„ฐ-์ฝ”๋”์— ์ „๋‹ฌํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํฌ์ŠคํŠธ-์ฝ”๋”๋Š” ์ „๋‹ฌ๋ฐ›์€ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹ค์ œ ๋น„ํŠธ์ŠคํŠธ๋ฆผ์„ ์ƒ์„ฑํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋‚ด์šฉ 4 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 8 ์ œ 2 ์žฅ ์ด์ „ ์—ฐ๊ตฌ 9 2.1 BTC 9 2.1.1 ๊ธฐ๋ณธ BTC ์•Œ๊ณ ๋ฆฌ์ฆ˜ 9 2.1.2 ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ BTC ์•Œ๊ณ ๋ฆฌ์ฆ˜ 10 2.2 SPIHT 13 2.2.1 1D SPIHT ์•Œ๊ณ ๋ฆฌ์ฆ˜ 13 2.2.2 SPIHT ํ•˜๋“œ์›จ์–ด 17 2.3 ์˜ˆ์ธก ๊ธฐ๋ฐ˜ ์ฝ”๋”ฉ 19 2.3.1 ์˜ˆ์ธก ๋ฐฉ๋ฒ• 19 2.3.2 VLC 20 2.3.3 ์˜ˆ์ธก ๊ธฐ๋ฐ˜ ์ฝ”๋”ฉ ํ•˜๋“œ์›จ์–ด 22 ์ œ 3 ์žฅ LCD ์˜ค๋ฒ„๋“œ๋ผ์ด๋ธŒ๋ฅผ ์œ„ํ•œ BTC 24 3.1 ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 24 3.1.1 ๋น„ํŠธ-์ ˆ์•ฝ ๋ฐฉ๋ฒ• 25 3.1.2 ๋ธ”๋ก ํฌ๊ธฐ ์„ ํƒ ๋ฐฉ๋ฒ• 29 3.1.3 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์š”์•ฝ 31 3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 33 3.2.1 ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์ธํ„ฐํŽ˜์ด์Šค 34 3.2.2 ์ธ์ฝ”๋”์™€ ๋””์ฝ”๋”์˜ ๊ตฌ์กฐ 37 3.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 44 3.3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ฑ๋Šฅ 44 3.3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ ๊ฒฐ๊ณผ 49 ์ œ 4 ์žฅ ์ €๋น„์šฉ ๊ทผ์ ‘-๋ฌด์†์‹ค ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์••์ถ•์„ ์œ„ํ•œ ๊ณ ์† 1D SPIHT 54 4.1 ์ธ์ฝ”๋” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 54 4.1.1 ์˜์กด ๊ด€๊ณ„ ๋ถ„์„ ๋ฐ ์ œ์•ˆํ•˜๋Š” ํŒŒ์ดํ”„๋ผ์ธ ์Šค์ผ€์ฅด 54 4.1.2 ๋ถ„๋ฅ˜ ๋น„ํŠธ ์žฌ๋ฐฐ์น˜ 57 4.2 ๋””์ฝ”๋” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 59 4.2.1 ๋น„ํŠธ์ŠคํŠธ๋ฆผ์˜ ์‹œ์ž‘ ์ฃผ์†Œ ๊ณ„์‚ฐ 59 4.2.2 ์ ˆ๋ฐ˜-ํŒจ์Šค ์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ• 63 4.3 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ 65 4.4 ์‹คํ—˜ ๊ฒฐ๊ณผ 73 ์ œ 5 ์žฅ ๊ณ ์ถฉ์‹ค๋„ RGBW ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ ๊ณ ์ • ์••์ถ•๋น„ VLC 81 5.1 ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 81 5.1.1 RGBW ์ธํ„ฐ-์ปฌ๋Ÿฌ ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ฐฉ์‹ 82 5.1.2 ๊ณ ์ • ์••์ถ•๋น„๋ฅผ ์œ„ํ•œ Golomb-Rice ์ฝ”๋”ฉ 85 5.1.3 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์š”์•ฝ 89 5.2 ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 90 5.2.1 ์ธ์ฝ”๋” ๊ตฌ์กฐ 91 5.2.2 ๋””์ฝ”๋” ๊ตฌ์กฐ 95 5.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 101 5.3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์‹คํ—˜ ๊ฒฐ๊ณผ 101 5.3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ ๊ฒฐ๊ณผ 107 ์ œ 6 ์žฅ ์••์ถ• ์„ฑ๋Šฅ ๋ฐ ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ ๋น„๊ต ๋ถ„์„ 113 6.1 ์••์ถ• ์„ฑ๋Šฅ ๋น„๊ต 113 6.2 ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ ๋น„๊ต 120 ์ œ 7 ์žฅ ๊ฒฐ๋ก  125 ์ฐธ๊ณ ๋ฌธํ—Œ 128 ABSTRACT 135Docto

    Image compression techniques using vector quantization

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    Bit inverting map method for improved steganography scheme

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    Achieving an efficient and accurate steganography scheme for hiding information is the foremost priority in the information and communication technology era. The developed scheme of hiding the secret message must capable of not giving any clue to the adversaries about the hidden data. In this regard, enhancing the security and capacity by maintaining the Peak Signal-to-Noise Ratio (PSNR) of the steganography scheme is the main issue to be addressed. This study proposes an improved Bit Inverting Map (BIM) method and a new scheme for embedding secret message into an image. This newly developed scheme is demonstrated to increase the security and capacity to resolve the existing problems. A binary text image is used to represent the secret message instead of normal text. Three stages implementations are used to select pixels before random embedding to select block of (64 64) pixels, followed by the Knight Tour algorithm to select sub-block of (8 8) pixels, and finally by the random pixels selection. The proposed BIM is distributed over the entire image to maintain high level of security against any kind of attack. One-bit indicator is used to decide if the secret bits are inserted directly or inversely, which enhanced the complexity of embedding process. Color and gray images from the standard dataset (USC-SIPI) including Lena, Peppers, Baboon, and Cameraman are implemented for benchmarking. Self-captured images are used to test the efficacy of the proposed BIM method. The results show good PSNR values of 72.9 and these findings verified the worthiness of the proposed BIM method. High complexities of pixels distribution and replacement of bits will ensure better security and robust imperceptibility compared to the existing scheme in the literature
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