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Chang Wen Chen - One of the best experts on this subject based on the ideXlab platform.

  • joint source channel rate control for pixel domain distributed video coding
    International Conference on Acoustics Speech and Signal Processing, 2011
    Co-Authors: Hu Chen, Eckehard Steinbach, Chang Wen Chen
    Abstract:

    We study the scenario of pixel-domain distributed video coding for noisy transmission environments and propose a method to allocate the available rate between source coding and channel coding to generate a robust video stream. Having observed in experiments the uncertainty of the source and the channel coding rate, we model them as random variables via offline training, estimate the decoding failure probability and calculate the mean end-to-end distortion. Adaptive Quantization is performed for each slice to minimize its mean end-to-end distortion. With this joint source-channel rate allocation, we compare the robustness of two coding prototypes, namely distributed video coding and distributed video coding with forward error correction. According to our experimental results, under same total bit budget, the distributed video coding only scheme proves more robust than the latter one and the gain is up to 1 dB in PSNR.

  • a scene Adaptive and signal Adaptive Quantization for subband image and video compression using wavelets
    IEEE Transactions on Circuits and Systems for Video Technology, 1997
    Co-Authors: Jiebo Luo, Chang Wen Chen, Kevin J Parker, Thomas S Huang
    Abstract:

    The discrete wavelet transform (DWT) provides an advantageous framework of multiresolution space-frequency representation with promising applications in image processing. The challenge as well as the opportunity in wavelet-based compression is to exploit the characteristics of the subband coefficients with respect to both spectral and spatial localities. A common problem with many existing Quantization methods is that the inherent image structures are severely distorted with coarse Quantization. Observation shows that subband coefficients with the same magnitude generally do not have the same perceptual importance. We propose in this paper a scene Adaptive and signal Adaptive Quantization scheme capable of exploiting the spectral and spatial localization properties resulting from the wavelet transform. The Quantization is implemented as maximum a posteriori probability estimation-based clustering in which subband coefficients are quantized to their cluster means, subject to local spatial constraints. The intensity distribution of each cluster within a subband is modeled by an optimal Laplacian source to achieve signal adaptivity, while spatial constraints are enforced by appropriate Gibbs random fields (GRF) to achieve scene adaptivity. With spatially isolated coefficients removed and clustered coefficients retained at the same time, the available bits are allocated to visually important scene structures so that the information loss is least perceptible. Furthermore, the reconstruction noise in the decompressed image can be suppressed using another GRF-based enhancement algorithm.

  • Face location in wavelet-based video compression for high perceptual quality videoconferencing
    IEEE Transactions on Circuits and Systems for Video Technology, 1996
    Co-Authors: Chang Wen Chen, K.j. Parker
    Abstract:

    We present a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. In addition to an Adaptive Quantization in which spatial constraints are enforced to preserve perceptually important information at low bit rates, semantic information of the human face is incorporated to design a hybrid compression scheme for videoconferencing since the human face is often the most important portion within a frame and should be coded with high fidelity. The human face is detected based on contour extraction and feature point analysis. An approximated face mask is then used in the Quantization of the decomposed subbands. At the same total bit rate, coarser Quantization of the background enables the face region to be quantized finer and coded with higher quality. Simulation results have shown that the perceptual image quality can be greatly improved using the proposed scheme.

  • Face location in wavelet-based video compression for high perceptual quality videoconferencing
    Proceedings. International Conference on Image Processing, 1995
    Co-Authors: Chang Wen Chen, K.j. Parker
    Abstract:

    We present a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. In addition to an Adaptive Quantization in which spatial constraints are enforced to preserve perceptually important information at low bit rates, semantic information of the human face is incorporated to design a hybrid compression scheme for videoconferencing, since the face is often the most important part and should be coded with high fidelity. The human face is detected based on contour extraction and feature point analysis. An approximate face mask is then used in the Quantization of the decomposed subbands. At the same total bit rate, coarser Quantization of the background enables the face region to be quantized finer and coded with a higher quality. Moreover, the resultant larger Quantization noise in the background can be suppressed using an edge-preserving enhancement algorithm. Experimental results have shown that the perceptual image quality is greatly improved using the proposed scheme.

Hong Zhao - One of the best experts on this subject based on the ideXlab platform.

  • A novel synchronization invariant audio watermarking scheme based on DWT and DCT
    IEEE Transactions on Signal Processing, 2006
    Co-Authors: X Y Wang, Hong Zhao
    Abstract:

    Synchronization attack is one of the key issues of digital audio watermarking. In this correspondence, a blind digital audio watermarking scheme against synchronization attack using Adaptive Quantization is proposed. The features of the proposed scheme are as follows: 1) a kind of more steady synchronization code and a new embedded strategy are adopted to resist the synchronization attack more effectively; 2) he multiresolution characteristics of discrete wavelet transform (DWT) and the energy-compression characteristics of discrete cosine transform (DCT) are combined to improve the transparency of digital watermark; 3) the watermark is embedded into the low frequency components by Adaptive Quantization according to human auditory masking; and 4) the scheme can extract the watermark without the help of the original digital audio signal. Experiment results shows that the proposed watermarking scheme is inaudible and robust against various signal processing such as noise adding, resampling, reQuantization, random cropping, and MPEG-1 Layer III (MP3) compression

K.j. Parker - One of the best experts on this subject based on the ideXlab platform.

  • Face location in wavelet-based video compression for high perceptual quality videoconferencing
    IEEE Transactions on Circuits and Systems for Video Technology, 1996
    Co-Authors: Chang Wen Chen, K.j. Parker
    Abstract:

    We present a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. In addition to an Adaptive Quantization in which spatial constraints are enforced to preserve perceptually important information at low bit rates, semantic information of the human face is incorporated to design a hybrid compression scheme for videoconferencing since the human face is often the most important portion within a frame and should be coded with high fidelity. The human face is detected based on contour extraction and feature point analysis. An approximated face mask is then used in the Quantization of the decomposed subbands. At the same total bit rate, coarser Quantization of the background enables the face region to be quantized finer and coded with higher quality. Simulation results have shown that the perceptual image quality can be greatly improved using the proposed scheme.

  • Face location in wavelet-based video compression for high perceptual quality videoconferencing
    Proceedings. International Conference on Image Processing, 1995
    Co-Authors: Chang Wen Chen, K.j. Parker
    Abstract:

    We present a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. In addition to an Adaptive Quantization in which spatial constraints are enforced to preserve perceptually important information at low bit rates, semantic information of the human face is incorporated to design a hybrid compression scheme for videoconferencing, since the face is often the most important part and should be coded with high fidelity. The human face is detected based on contour extraction and feature point analysis. An approximate face mask is then used in the Quantization of the decomposed subbands. At the same total bit rate, coarser Quantization of the background enables the face region to be quantized finer and coded with a higher quality. Moreover, the resultant larger Quantization noise in the background can be suppressed using an edge-preserving enhancement algorithm. Experimental results have shown that the perceptual image quality is greatly improved using the proposed scheme.

Huazhong Yang - One of the best experts on this subject based on the ideXlab platform.

  • a norm space Adaptive and blind audio watermarking algorithm by discrete wavelet transform
    Signal Processing, 2013
    Co-Authors: Xinkai Wang, Pengjun Wang, Peng Zhang, Huazhong Yang
    Abstract:

    In this paper, combining the robustness of vector norm with that of the approximation components after the discrete wavelet transform (DWT), a blind and Adaptive audio watermarking algorithm is proposed. In order to improve the robustness and imperceptibility, a binary image encrypted by Arnold transform as watermark is embedded in the vector norm of the segmented approximation components, the count of which depends on the size of the watermark image, after DWT of the original audio signal through Quantization index modulation (QIM) with an Adaptive Quantization step selection scheme. Moreover, a detailed method has been designed to search the suitable Quantization step parameters. Experimental results indicate that even though the capacity of the proposed algorithm is high, up to 102.4bps, this algorithm is still able to maintain good quality of the audio signal and tolerate a wide class of common attacks such as additive white Gaussian noise (AWGN), Gaussian Low-pass filter, Kaiser Low-pass filter, resampling, requantizing, cutting, MP3 compression and echo.

Zhao Yuanyuan - One of the best experts on this subject based on the ideXlab platform.

  • improved Quantization watermarking with an Adaptive Quantization step size and hvs
    International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2005
    Co-Authors: Zhao Yuanyuan
    Abstract:

    This paper proposes a new image-Adaptive watermarking technique which utilizes a new combination of an Adaptive Quantization step size and a HVS(human visual system) model in the wavelet domain. Here we use Quantization Index Modulation(QIM) method with an Adaptive Quantization step size to realize the embedding scheme. The HVS masking is accomplished pixel by pixel by take into account the luminance and the frequency content of all the image subbands. The watermarking consists of a pseudorandom sequence which is Adaptively embedded into the subbands. As usual, the watermark bits are detected by a minimum distance detector. Experimental results prove the effectiveness of the new algorithm.