Statistical analysis of the DCT coefficients and their quantization error

Yovanof, Gregory S.; Liu, S.

doi:10.1109/acssc.1996.601095

Cited by 36 publications

(71 citation statements)

References 9 publications

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“…This statistically based model of the error signal is referred to as a quantization noise model since the error signal represents unwanted information in the resulting image representation. In the context of DCT-based image compression, there are numerous cases that treat the compression error as a random quantity; some do so in order to analyze visibility [18] or characteristics [36] of the error, while others do so in order to formulate algorithms that attempt to remove the noise [14,15,35]. This work is interested in both the characterization and the alleviation of the compression noise.…”

Section: Article In Pressmentioning

confidence: 99%

Temporal filtering for restoration of wavelet-compressed motion imagery

Robertson

2004

Signal Processing: Image Communication

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Section: Article In Pressmentioning

confidence: 99%

Temporal filtering for restoration of wavelet-compressed motion imagery

Robertson

2004

Signal Processing: Image Communication

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“…[10], [7], [11] that the residual follows a Laplacian distribution. It is noted that some other distributions are proposed for estimating the true residual histogram, e.g., Cachy distribution [31], [32] and Generalized Gaussian distribution [33], [34]. The authors in Ref.…”

Section: A Derivation Of Source Coding Bit Rate Function 1) Source Cmentioning

confidence: 99%

Rate-Distortion Optimized Cross-Layer Rate Control in Wireless Video Communication

Chen

2012

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A wireless video communication system can be designed based on the rate-distortion (R-D) criterion, i.e., minimizing the end-to-end distortion (which includes quantization distortion and transmission distortion) subject to the transmission bit-rate constraint. The minimization can be achieved by adjusting the source encoding parameters and channel encoding parameters. This R-D optimization (RDO) is usually done for each video frame individually in a real-time video communication system, e.g. video calls or videoconferencing. To achieve this, an accurate bit-rate model and distortion model for each frame can be used to reduce the RDO complexity. In this paper, we derive a source bit-rate model and quantization distortion model; we also improve the performance bound for channel coding under a convolutional code and a Viterbi decoder, and derive its performance bound under a Rayleigh block fading channel. Given the instantaneous channel condition, e.g., signal-to-noise ratio (SNR) and transmission bit-rate constraint, we design an R-D optimized cross-layer rate control (CLRC) algorithm by jointly choosing quantization step size in source coding and code rate in channel coding. Experimental results show that our proposed R-D models are more accurate than the existing R-D models. Experimental results also showed that the rate control under our models has more stable R-D performance than the existing rate control algorithms; using the channel estimation, CLRC can further achieve remarkable R-D performance gain over that without channel estimation. Another important result is that the subjective quality of our CLRC algorithm is much better than the existing algorithms due to its intelligent reference frame selection.

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“…It also requires a high computational complexity. In order to avoid this problem, some researches have been studied to find the optimal shape factor with small complexity [7], [8]. In this work, we simply decide the shape factor by a similarity between two adjacent images.…”

Section: A Shape Factormentioning

confidence: 99%

A New Quantization for Rate Control with Frame Variation Consideration

Kim

Lim

2006

2006 IEEE International Conference on Multimedia and Expo

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Abstract-The main role of rate control in video coding is to handle a trade-off between a bandwidth limit and a video quality. In the low-delay video communication systems, its responsibility is more important. In this work, we propose a new rate control algorithm. In this paper, we have two approaches: one is a consideration of variations among the frames at the framelevel bit allocation, and the other is a model-based adaptive quantization at the macroblock-level. The proposed scheme is done with a low computational complexity. For simulations, we implement the proposed algorithm to H.263 video codec since it is widely used as a video coding tool in real-time mobile AV systems. The proposed scheme shows more improved picture quality than H.263 TMN8 and generates coding bits close to the target bits. The results also show more regular PSNR fluctuation than H.263 TMN8.

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Statistical analysis of the DCT coefficients and their quantization error

Cited by 36 publications

References 9 publications

Temporal filtering for restoration of wavelet-compressed motion imagery

Temporal filtering for restoration of wavelet-compressed motion imagery

Rate-Distortion Optimized Cross-Layer Rate Control in Wireless Video Communication

A New Quantization for Rate Control with Frame Variation Consideration

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