Quantization (image processing)

About: Quantization (image processing) is a research topic. Over the lifetime, 7977 publications have been published within this topic receiving 126632 citations.

Data hiding technique in JPEG compressed domain

Abstract: JPEG is a common image format in the world wide web. JPEG-compressed images can be used to hide data for secret internet communication and simply any auxiliary data. In this paper, we propose an algorithm called J-Mark to embed invisible watermark information into JPEG compressed images in the compress domain. There are three parts of J-Mark: block selection, DCT coefficient selection, and modification of selected DCT coefficients. Only the texture blocks with significant masking properties are selected in block selection. Only the DCT coefficients with significant energy in the selected blocks are selected. The watermark data are embedded as the 'randomized parity' in the selected DCT coefficients. The embedded data can be recovered perfectly in the compressed domain without fully decoding the JPEG image. Experiment results suggest that the proposed J-Mark can hide the watermarking data without detectable visual artifacts. Although the data hiding capacity differs among images, some parameter of J-Mark can be used to achieve tradeoff between data hiding capacity and visual quality.

A high payload data hiding scheme based on modified AMBTC technique

Abstract: In this paper, we propose a data hiding scheme which uses our modified AMBTC compression technique for embedding the secret data. Our modified AMBTC technique converts the one bit plane into two bit plane which helps in achieving better quality compressed image as well as high capacity. In this scheme, we first apply the original AMBTC technique on the given cover image then identify the smooth and complex blocks using a user defined threshold value. In case of the smooth blocks, it converts the one bit plane into two bit plane using mean value of the block and replaces all the bits of the bit plane with the secret data bits. It calculates four quantization levels in place of two old quantization levels. In case of complex blocks, it converts the one bit plane into two bit plane but here only the first LSBs of the newly constructed bit plane is replaced by the secret data bits. The four new quantization levels are calculated using the resultant bit plane. Thus, this scheme is able to embed 2 bits into each pixel of the smooth blocks and one bit in each pixel of complex blocks. It provides good quality stego image because the introduced error during the secret data embedding is reduced by having four quantization levels. Experimentally, our scheme is superior to the existing AMBTC based data hiding schemes in terms of both data hiding capacity and image quality. In fact, the proposed scheme hides approximately two times more secret data than the existing schemes with better image quality.

Image compression using fractals and discrete cosine transform

Abstract: A new image data compression method using both fractals and the discrete cosine transform (DCT) is presented. The original image is first encoded by fractals in the DCT domain, then the error image is encoded using the DCT. Experiments show that the method can achieve high fidelity at a high compression ratio.

A novel approach for increasing security and data embedding capacity in images for data hiding applications

Abstract: Data hiding in multimedia is the process of secretly embedding information into data sources such as image, video, or audio signals without changing the perceptual quality of the data source. We present a novel blind data hiding technique for hiding information in still images. This technique is based on embedding the information in the transform domain, after decorrelating the samples in the spatial domain. This results in a significant increase in the number of transform coefficients that can be used to transmit the hidden information. The technique is suitable for a variety of data hiding applications such as steganography, data authentication and captioning. The technique achieves a higher and more secure data embedding rate than existing data embedding transform domain techniques developed for these particular applications.

MLP for adaptive postprocessing block-coded images

Abstract: A new technique based on the multilayer perceptron (MLP) neural network is proposed for blocking-artifact removal in block-coded images. The new method is based on the concept of learning-by-examples. The compressed image and its original uncompressed version are used to train the neural networks. In the developed scheme, inter-block slopes of the compressed image are used as input, the difference between the original uncompressed and the compressed image is used as desired output for training the networks. Blocking-artifact removal is realized by adding the neural network's outputs to the compressed image. The new technique has been applied to process JPEG compressed images. Experimental results show significant improvements in both visual quality and peak signal-to-noise ratio. It is also shown the present method is comparable to other state of the art techniques for quality enhancement in block-coded images.