A Study of trellis coded quantization for image compression

Trellis coded quantization has recently evolved as a powerful quantization technique in the world of lossy image compression. The aim of this thesis is to investigate the potential of trellis coded quantization in conjunction with two of the most popular image transforms today; the discrete cosine transform and the discrete wavelet trans form. Trellis coded quantization is compared with traditional scalar quantization. The 4-state and the 8-state trellis coded quantizers are compared in an attempt to come up with a quantifiable difference in their performances. The use of pdf-optimized quantizers for trellis coded quantization is also studied. Results for the simulations performed on two gray-scale images at an uncoded bit rate of 0.48 bits/pixel are presented by way of reconstructed images and the respective peak signal-to-noise ratios. It is evident from the results obtained that trellis coded quantization outperforms scalar quantization in both the discrete cosine transform and the discrete wavelet transform domains. The reconstructed images suggest that there does not seem to be any considerable gain in going from a 4-state to a 8-state trellis coded quantizer. Results also suggest that considerable gain can be had by employing pdf-optimized quantizers for trellis coded quantization instead of uniform quantizers

Performance of gray scaled images using segmented cellular neural network - cellular neural network combined trellis coded quantization / modulation (SCNN - CNN CTCQ/TCM) approach over rician fading channel

In this paper, Segmented Cellular Neural Network-Cellular Neural Network Combined Trellis Coded Quantization / Modulation (SCNN-CNN CTCQ/TCM) scheme is introduced. Here, a gray scaled image is lowered to 3 bit using our proposed Segmented Cellular Neural Network approach (SCNN) and then passed through a new CNN based structure which models combined trellis coded quantization / modulation. The performance of our combined scheme has been analyzed over Rician fading channel. Computer simulations studies confirm the analytical upper bound curves.Bu çalışmada Bölütlenmiş Hücresel Yapay Sinir Ağları-Hücresel Yapay Sinir Ağları Birleşik Kafes Kodlamalı Kuantalama ve Modülasyon işleminin gerçekleştirildiği yeni bir yapı tanıtılmıştır. Burada gri tonlamalı bir görüntü bizim tarafımızdan önerilen Bölütlemeli Hücresel Yapay Sinir Ağı yaklaşımı kullanılarak 3 bit seviyesine düşürülmüş ve daha sonra CNN tabanlı bir modelden oluşmuş kafes kodlamalı kuantalama ve modülasyon yapısından geçirilmiştir. Son olarak bu önerilen yapının performans analiz işlemleri yapılarak simulasyon ve analitik hata başarım eğrileri elde edilmiştir

Performance of Gray Scaled Images Using Segmented Cellular Neural Network - Cellular Neural Network Combined Trellis Coded Quantization / Modulation (SCNN - CNN CTCQ/TCM) Approach Over Rician Fading Channel

Bu çalışmada Bölütlenmiş Hücresel Yapay Sinir Ağları-Hücresel Yapay Sinir Ağları Birleşik Kafes Kodlamalı Kuantalama ve Modülasyon işleminin gerçekleştirildiği yeni bir yapı tanıtılmıştır. Burada gri tonlamalı bir görüntü bizim tarafımızdan önerilen Bölütlemeli Hücresel Yapay Sinir Ağı yaklaşımı kullanılarak 3 bit seviyesine düşürülmüş ve daha sonra CNN tabanlı bir modelden oluşmuş kafes kodlamalı kuantalama ve modülasyon yapısından geçirilmiştir. Son olarak bu önerilen yapının performans analiz işlemleri yapılarak simulasyon ve analitik hata başarım eğrileri elde edilmiştir.In this paper, Segmented Cellular Neural Network-Cellular Neural Network Combined Trellis Coded Quantization / Modulation (SCNN-CNN CTCQ/TCM) scheme is introduced. Here, a gray scaled image is lowered to 3 bit using our proposed Segmented Cellular Neural Network approach (SCNN) and then passed through a new CNN based structure which models combined trellis coded quantization / modulation. The performance of our combined scheme has been analyzed over Rician fading channel. Computer simulations studies confirm the analytical upper bound curves

Performance of Gray Scaled Images Using Segmented Cellular Neural Network - Cellular Neural Network Combined Trellis Coded Quantization / Modulation (SCNN - CNN CTCQ/TCM) Approach Over Rician Fading Channel

Bu çalışmada Bölütlenmiş Hücresel Yapay Sinir Ağları-Hücresel Yapay Sinir Ağları Birleşik Kafes Kodlamalı Kuantalama ve Modülasyon işleminin gerçekleştirildiği yeni bir yapı tanıtılmıştır. Burada gri tonlamalı bir görüntü bizim tarafımızdan önerilen Bölütlemeli Hücresel Yapay Sinir Ağı yaklaşımı kullanılarak 3 bit seviyesine düşürülmüş ve daha sonra CNN tabanlı bir modelden oluşmuş kafes kodlamalı kuantalama ve modülasyon yapısından geçirilmiştir. Son olarak bu önerilen yapının performans analiz işlemleri yapılarak simulasyon ve analitik hata başarım eğrileri elde edilmiştir.In this paper, Segmented Cellular Neural Network-Cellular Neural Network Combined Trellis Coded Quantization / Modulation (SCNN-CNN CTCQ/TCM) scheme is introduced. Here, a gray scaled image is lowered to 3 bit using our proposed Segmented Cellular Neural Network approach (SCNN) and then passed through a new CNN based structure which models combined trellis coded quantization / modulation. The performance of our combined scheme has been analyzed over Rician fading channel. Computer simulations studies confirm the analytical upper bound curves

Informed stego-systems in active warden context: statistical undetectability and capacity

Several authors have studied stego-systems based on Costa scheme, but just a few ones gave both theoretical and experimental justifications of these schemes performance in an active warden context. We provide in this paper a steganographic and comparative study of three informed stego-systems in active warden context: scalar Costa scheme, trellis-coded quantization and spread transform scalar Costa scheme. By leading on analytical formulations and on experimental evaluations, we show the advantages and limits of each scheme in term of statistical undetectability and capacity in the case of active warden. Such as the undetectability is given by the distance between the stego-signal and the cover distance. It is measured by the Kullback-Leibler distance.Comment: 6 pages, 8 figure