In this paper, we present a novel data-embedding system with high embedding capacity. The embedding algorithm is based on the quantized projection embedding method with some enhancement to achieve high embedding rates. In particular, our system uses a random permutation of the columns of a Hadamard matrix as projection vectors and a fixed perceptual mask based on the JPEG default quantization table for the quantization step design. As a result, the data-embedding system achieves 1/167 (1 bit out of 167 raw image bits) to 1/84 hiding ratios with a BER of around 0.1% in the presence of JPEG compression attacks, while maintaining visual distortion at a minimum.

The integration of multimedia services into wireless communication networks is a major source of future technological advances. One of the main challenging issues in this endeavor is the resource optimization strategy. This paper addresses this issue from the perspective of minimizing the total power consumption of a mobile terminal while maintaining a guaranteed quality-of-service (QoS). For many years, the management strategy has dealt primarily with bandwidth allocation, network capacity, and QoS. However, due to the integration of multimedia services, the increasing energy consumption of a mobile unit is also becoming a dominant factor in the design of communication systems. In this paper, we describe two technologies that can make a wireless multimedia communication system more energy-efficient while ensuring QoS. These technologies consist of an energy-efficient communication protocol for the uplink channel and a low-complexity multirate transmission scheme. We also provide a video transmission example using the H.263 standard in the proposed system to demonstrate the importance of our total power optimization strategy. The simulation results show that a savings of 10-32% is achieved in the total energy consumption of the mobile unit.

A complexity scalable algorithm is an algorithm that has several computation scales with gracefully degraded qualities from the original quality. We present the importance of, and the design principles and guidelines for, making a scalable algorithm. We then use a resolution enhancement algorithm as an example and make it scalable. The resulting scalable algorithm has 5 computation scales, consisting of 35%, 31%, 23%, 16%, and 0% of the original optimized computation with very-good quality compared to the non-enhanced quality. This scalable algorithm has many applications in multi-feature multimedia HD (high definition) devices.

Video processing on programmable platforms offers significant advantages over dedicated hardware such as shorter development cycles, flexibility, and upgradability, among others. Although the emergence of powerful media processors is making video processing on programmable platforms closer to reality, practical media-processor-based systems benefit from complexity-scalable video processing algorithms that can trade off video quality against computation while ensuring real-time performance. We develop complexity-scalable MPEG-2 video decoding algorithms with both SNR quality degradation and decoding resolution reduction. We improve the decoder's complexity-quality performance by employing signal-adaptive processing in decoding blocks and picture-type-dependent processing with unequal computation resource allocation to exploit each picture type's varying impact on the overall video quality. Realizing that software video decoding operates in a dynamic environment where both the actual computation (depending on video data) and computation allocation (depending on other functions running simultaneously) vary, we develop dynamic complexity regulation techniques to ensure that the decoding complexity stays within allocation. Simulation results on complexity-scalable MPEG-2 video decoding and dynamic complexity regulation on media processors are presented.

Current implementations of real-time video decoders employ hard-decoding without run-time computation regulation. The result is fluctuation in the video decoding time that requires over-specified dedicated hardware or general-purpose processors to guarantee real-time performance. We propose a computation regulation system for MPEG-2 video decoding on media processors that obviates over-engineering while maintaining real-time performance and video quality. Our computation regulation scheme consists of dynamic complexity prediction and control. The complexity prediction algorithm takes implementation issues specific to media processors into account and yields accurate prediction results that correlate with actual measurements to 97%. The predicted complexity is then used as a control signal to adjust the decoding algorithm so that peaks in computation load are suppressed. Simulation results show that our system can lower the overall CPU cycle budget by a large factor (25%) with very little degradation in video quality.

We present a novel algorithm for high capacity data embedding. The proposed algorithm gives high hiding ratios up to 1/13 (1 embedded bit out of 13 raw image pixels) subject to JPEG compression with quality factor equals 75. With such high capacity, one can easily embed some important regions of an image inside the image itself with very small perceptual distortion. In the proposed algorithm, the DCT coefficients of 8/spl times/8 blocks are first rearranged using Hilbert curves. Data is embedded using projections on a random orthogonal set. The algorithm recovers the embedded data without any reference to the original image, with very low BER (around 0.1%). Finally, the proposed algorithm shows robustness to JPEG compression.

In this work we describe a simple transform-coefficient sorting algorithm that enhances the performance of image compression techniques. We use multiresolution grids to localize significant pixels and send out pixel values using successive approximation. In the wavelet domain our method performs slightly better than SPIHT (in average 0.1 dB of PSNR). In the DCT domain our method outperforms the SPIHT-based method and the significant tree quantization method by 1 dB. Our approach breaks the dominant role played by the zero tree structure in image coding, and provides a low complexity solution to image compression.

In this work we propose a novel perceptual coding strategy to encode images at low bit rates. Our approach encodes the most important visual features of an image extracted by using a sigma filter. Preliminary results show that the image decoded by our scheme contains less visual artifacts than the SPIHT decoded image at low bit rates, and is very pleasant to human eyes. Unlike other low bit rate progressive coding methods, in which the low-bit-rate decoded image quality is good only at low resolution, our scheme provides good perceptual quality regardless of resolution sizes. Our method provides a promising way to make image browsing faster while maintaining good image perceptual quality.

Article Fraud detection and self embedding Share on Authors: Tse-Hua Lan Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MinnesotaView Profile , Ahmed H. Tewfik Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MinnesotaView Profile Authors Info & Claims MULTIMEDIA '99: Proceedings of the seventh ACM international conference on Multimedia (Part 2)October 1999 Pages 33–36https://doi.org/10.1145/319878.319887Online:01 October 1999Publication History 8citation660DownloadsMetricsTotal Citations8Total Downloads660Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We study how to use the H.263 video communications standard efficiently to save the total consumed energy of a mobile unit in cellular networks. Particularly, we study the computational power dissipation of various operation modes available in the H.263 coding standard. We show how to achieve low power consumption in a mobile unit by judiciously selecting the operating mode of H.263 in response to the mobile environment changes (e.g., slow fading and path loss), while maintaining a good video quality level. Our preliminary results show that a mobile with the proposed method consumes 10% less energy than a mobile that comes with the most efficient power-rate mode (with a search window size of 8) of H.263 video coding in one-hour mobile communication simulation. It even saves around 32% energy over a mobile operating with an advanced coding method (using four negotiating options).

We study the problem of jointly optimizing source/channel/modulation coding to achieve a given quality of service while minimizing the total power consumption of mobiles. This paper emphasizes the design of a robust transmission framework in the context of a total power minimization scheme. We design a simple but efficient transmission scheme which is called multistage coded modulation that combines source/channel (S/C) coding and multirate modulation (MM). We then integrate into this scheme a complexity-scalable video coder to achieve the best trade-offs in multimedia wireless communications between total power, quality of service, and capacity. Preliminary results show superior video frame quality (around 3 dB better than the simple S/C coding scheme) of the proposed system in the severe channel condition, with total power consumption kept to a minimum.

In this paper, we introduce a novel approach for adaptive minimization of the total energy consumption in multimedia wireless communications subject to achieving a given quality of service. Our approach exploits trade-offs between the effects of energy consumed in processing (source and channel coding) and energy consumed in transmission under different noise and channel conditions, on the received quality of the multimedia. We present several simulation results involving image transmission that illustrate the energy consumption savings that can be achieved using our proposed approach.