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.

A Selective Video Encryption Scheme for MPEG Compression Standard

Abstract: With the increase of commercial multimedia applications using digital video, the security of video data becomes more and more important. Although several techniques have been proposed in order to protect these video data, they provide limited security or introduce significant overhead. This paper proposes a video security scheme for MPEG video compression standard, which includes two methods: DCEA (DC Coefficient Encryption Algorithm) and "Event Shuffle." DCEA is aim to encrypt group of codewords of DC coefficients. The feature of this method is the usage of data permutation to scatter the ciphertexts of additional codes in DC codewords. These additional codes are encrypted by block cipher previously. With the combination of these algorithms, the method provides enough security for important DC component of MPEG video data. "Event Shuffle" is aim to encrypt the AC coefficients. The prominent feature of this method is a shuffling of AC events generated after DCT transformation and quantization stages. Experimental results show that these methods introduce no bit overhead to MPEG bit stream while achieving low processing overhead to MPEG codec.

Learning Efficient Binary Codes From High-Level Feature Representations for Multilabel Image Retrieval

Abstract: Due to the efficiency and effectiveness of hashing technologies, they have become increasingly popular in large-scale image semantic retrieval. However, existing hash methods suppose that the data distributions satisfy the manifold assumption that semantic similar samples tend to lie on a low-dimensional manifold, which will be weakened due to the large intraclass variation. Moreover, these methods learn hash functions by relaxing the discrete constraints on binary codes to real value, which will introduce large quantization loss. To tackle the above problems, this paper proposes a novel unsupervised hashing algorithm to learn efficient binary codes from high-level feature representations. More specifically, we explore nonnegative matrix factorization for learning high-level visual features. Ultimately, binary codes are generated by performing binary quantization in the high-level feature representations space, which will map images with similar (visually or semantically) high-level feature representations to similar binary codes. To solve the corresponding optimization problem involving nonnegative and discrete variables, we develop an efficient optimization algorithm to reduce quantization loss with guaranteed convergence in theory. Extensive experiments show that our proposed method outperforms the state-of-the-art hashing methods on several multilabel real-world image datasets.

Quality Scalability Aware Watermarking for Visual Content

Abstract: Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.

An efficient compact Tchebichef Moment for image compression

Abstract: Orthogonal moment functions have long been used in image analysis. This paper proposes a novel approach based on discrete orthogonal Tchebichef Moment for efficient image compression. The method incorporates simplified mathematical framework techniques using matrices, as well as a block-wise reconstruction technique to eliminate possible occurrences of numerical instabilities at higher moment orders. The comparison between Tchebichef Moment compression and JPEG compression has been done. The results show significant advantages for Tchebichef Moment in terms of its image quality and compression rate. Tchebichef moment provides a more compact support to the image via sub-block reconstruction for compression. Tchebichef Moment Compression has clear potential to perform better for broader domain on real digital images and graphically generated images.

An integrated progressive image coding and watermark system

Abstract: The design of an integrated image coding and watermark system with the wavelet transform is examined in this work. First, the multi-threshold wavelet codec (MTWC) is used to achieve the image compression purpose. Unlike other embedded wavelet coders which use a single initial threshold in their successive approximate quantization (SAQ), MTWC adopts different initial thresholds in different subbands. A superior rate-distortion tradeoff is achieved by MTWC with a low computational complexity. Then, a non-invertible progressive watermark scheme is incorporated in MTWC for copyright protection. This watermark scheme uses the user input data to produce a Gaussian distribution pseudorandom watermark in the wavelet domain. The performance of the proposed watermark technology is supported by experimental results.