Features-pooling blind jpeg image steganalysis

In this research, we introduce a new blind steganalysis in detecting grayscale JPEG images. Features-pooling method is employed to extract the steganalytic features and the classification is done by using neural network. Three different steganographic models are tested and classification results are compared to the five state-of-the-art blind steganalysis.

JPEG image steganalysis improvement via image-to-image variation minimization

In this research, we introduce an approach to enhance the discriminative capability of features by employing image-to-image variation minimization. In order to minimize image-to-image variation, we will estimate the cover image from the stego image by decompressing the stego image, transforming the decompressed image and recompressing back. Since the effect of the embedding operation in an image steganography is actually a noise adding process to the image, applying these three processes will smooth out the noise and hence the estimated cover image can be obtained.

GAP-RBF based NR image quality measurement for JPEG coded images

In this paper, we present a growing and pruning radial basis function based no-reference (NR) image quality model for JPEG-coded images. The quality of the images are estimated without referring to their original images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the problem of quality estimation is transformed to a function approximation problem and solved using GAP-RBF network. GAP-RBF network uses sequential learning algorithm to approximate the functional relationship. The computational complexity and memory requirement are less in GAP-RBF algorithm compared to other batch learning algorithms. Also, the GAP-RBF algorithm finds a compact image quality model and does not require retraining when the new image samples are presented. Experimental results prove that the GAP-RBF image quality model does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.

高性能全流水线可控参数JPEG-LS编码器实现

提出了一种高性能的JPEG-LS无损/近无损图像压缩算法VLSI实现结构.通过对JPEG-LS算法瓶颈的分析,针对算法中不利于流水线实现的场景缓存部分,采用了一种信号量集机制避免流水线等待.全流水线结构保证了算法实现可以满足高速图像传感器系统的吞吐量需求.同时通过高度参数化的设计,系统可以动态调整和优化算法参数,使压缩效果和效率适应不同的运行环境.算法在FPGA平台通过验证,并得到了接近甚至超过其他A-SIC实现的性能.

JPEG压缩编码的扩充自适应量化器设计

在ＪＰＥＧ静止图象压缩的基础上，设计了一种扩充的自适应量化器．利用人眼的视觉特征，通过分析ＭＣＵ块的局部视觉活动性，以ＭＣＵ活动性函数确定量化因子，并引入亮度掩盖算子调节量化参量．实验结果表明，本文所设计的自适应量化器能减少图象编码主观失真，改善图象质量，获得更好的压缩效果

JPEG encoder system-on-a-chip demonstrator

The design of a System-on-a-Chip (SoC) demonstrator for a baseline JPEG encoder core is presented. This combines a highly optimized Discrete Cosine Transform (DCT) and quantization unit with an entropy coder which has been realized using off-the-shelf synthesizable IP cores (Run-length coder, Huffman coder and data packer). When synthesized in a 0.35 µm CMOS process, the core can operate at speeds up to 100 MHz and contains 50 k gates plus 11.5 kbits of RAM. This is approximately 20% less than similar JPEG encoder designs reported in literature. When targeted at FPGA the core can operate up to 30 MHz and is capable of compressing 9-bit full-frame color input data at NTSC or PAL rates.