Dynamic Computational Complexity and Bit Allocation for Optimizing H.264/AVC Video Compression

Kaminsky, Evgeny; Grois, Dan; Hadar, Ofer

doi:10.1109/itre.2006.381556

Cited by 6 publications

(10 citation statements)

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“…Figure 6 shows the PSNR and CR evolution, frame-by-frame, throughout the compression of the first 100 frames of the Foreman video, while Figure 7 shows the growth in the number of neurons during this compression. In Table 3 the results for HEO-II refers to [14], KAMINSKY and JM9.5 refer to [15], and FS and ANEA refer to [16]. NMC1 refers to the results for SMNN with t=0.6, F=5.0, and a window with 8×8 pixels size, and NMC2 refers to results for SMNN with t=0.8, F=20.0, and a window with 4×4 pixels size.…”

Section: Fig 4 Dog Lisbela In Different Images Formats Obtained Witmentioning

confidence: 99%

Grayscale Images and RGB Video: Compression by Morphological Neural Network

Souza

Cortez

Silva

2012

Artificial Neural Networks in Pattern Recognition

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Abstract. This paper investigates image and RGB video compression by a supervised morphological neural network. This network was originally designed to compress grayscale image and was then extended to RGB video. It supports two kinds of thresholds: a pixel-component threshold and pixel-error counting threshold. The activation function is based on an adaptive morphological neuron, which produces suitable compression rates even when working with three color channels simultaneously. Both intra-frame and interframe compression approaches are implemented. The PSNR level indicates that the compressed video is compliant with the desired quality levels. Our results are compared to those obtained with commonly used image and video compression methods. Network application results are presented for grayscale images and RGB video with a 352 × 288 pixel size.

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Section: Fig 4 Dog Lisbela In Different Images Formats Obtained Witmentioning

confidence: 99%

Grayscale Images and RGB Video: Compression by Morphological Neural Network

Souza

Cortez

Silva

2012

Artificial Neural Networks in Pattern Recognition

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“…Our novel scheme is based on our SVC Computational Complexity-Rate-Distortion model (SVC C-R-D model), which is presented in detail in Section 3. It should be noted that we have extended the C-R-D model presented in our previous research [2], [6] to support the SVC systems.…”

Section: Novel Region-of-interest Scalable Video Coding Pre-filteringmentioning

confidence: 94%

Complexity-aware adaptive bit-rate control with dynamic ROI pre-processing for scalable video coding

Grois

Hadar

2011

2011 IEEE International Conference on Multimedia and Expo

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We present a novel efficient complexity-aware adaptive bitrate control with dynamic Region-of-Interest pre-processing (pre-filtering) for the Scalable Video Coding (SVC), which is an extension of H.264/AVC. According to the proposed approach, we adaptively vary various parameters of the SVC pre-filters, such as standard deviations and a number of filters for the dynamic pre-processing of a transition region between the ROI and background, thereby enabling to dynamically adjust the desired SVC settings. Our adaptive bit-rate control is based on an SVC computational complexity-rate-distortion (C-R-D) analysis, thereby adding a complexity dimension to the conventional Region-of-Interest SVC rate-distortion analysis. As a result, the ROI SVC visual presentation quality is significantly improved, which can be especially useful for various resource-limited devices, such as mobile devices. The performance of the presented adaptive ROI SVC preprocessing scheme is evaluated and tested in detail, further comparing it to the Joint Scalable Video Model reference software (JSVM 9.19) and demonstrating significant improvements both in quality and bit-rate.Index Terms -Regions-of-interest video coding, preprocessing/pre-filtering, bit-rate control, image/video coding, Regions-of-interest scalability, high-quality visual presentation, Scalable Video Coding, H.264/AVC.

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“…d. Estimate a set of groups of coding modes (e.g., modes such as Inter-Search16X8, InterSearch8X16, Inter-Search8X8, Inter-Search8X4, Inter-Search4X8, Inter-Search4X4 modes, and the like) of the current macroblock in the current frame within the above SVC layer by using the actual group of coding modes for the macroblocks in the co-located positions of the previous frame(s) and the actual group of coding modes of neighbor macroblocks in the current frame. e. Compute the corresponding QPs by using a quadratic model (Chiang & Zhang, 1997;Kaminsky et al, 2008;Grois et al, 2010c). f. Perform the Rate-Distortion Optimization (RDO) for each MB by using the QPs derived from the above step 5. g. Adaptively adjust the QPs (increase/decrease the QPs by a predefined quantization step size) according to the current overall bit-rate.…”

Section: Bit-rate Control For Region-of-interest Codingmentioning

confidence: 99%