Dan Grois

Dynamic computational complexity and bit allocation for optimizing H.264/AVC video compression

In this work we present a novel approach for optimizing H.264/AVC video compression by dynamically allocating computational complexity (such as a number of CPU clocks) and bits for encoding each coding element (basic unit) within a video sequence, according to its predicted MAD (mean absolute difference). Our approach is based on a computational complexity-rate-distortion (C-R-D) analysis, which adds a complexity dimension to the conventional rate-distortion (R-D) analysis. Both theoretically and experimentally, we prove that by implementing the proposed approach better results are achieved. In addition, we present a method and system for implementing the proposed approach, and for controlling computational complexity and bit allocation in real-time and off-line video coding. For allocating a corresponding group of coding modes and the quantization step-size, we develop computational complexity - complexity step - rate (C-I-R) and rate - quantization step-size - computational complexity (R-Q-C) models.

ROI adaptive scalable video coding for limited bandwidth wireless networks

Much of the attention in the field of video adaptation has been directed to the Scalable Video Coding (SVC), which is the extension of the H.264/AVC standard, since the bit-stream scalability for video is a desirable feature for many multimedia applications. The need for the scalability mainly arises from the need for spatial formats, bit rates or power. To fulfill these requirements, it would be beneficial to simultaneously transmit or store video in variety of spatial/temporal resolutions and qualities, leading to the video bit-stream scalability. Regions-of-interest (ROI) coding is a desirable feature in future applications of Scalable Video Coding. For those SVC applications, users at the decoder side usually wish to receive a high-quality decoded video stream, containing the desired ROI, which should be adaptively selected from the pre-encoded scalable bit-stream. In this work, we present a novel ROI adaptive scalable video coding scheme, enabling to adaptively set desirable ROI location, size, resolution and bit-rate, according to the limited network bandwidth and predefined settings. This, in turn, will enable providing an effective rate control for multiple ROIs, thereby enabling adaptively selecting the required ROI from multiple ROIs in the scalable bit-stream, and adaptively changing ROI spatial resolution, ROI visual quality or amount of bits allocated for each ROI, according to the network bandwidth and users settings (i.e., users display resolution, etc.).

Adaptive bit-rate control for Region-of-Interest Scalable Video Coding

In this work, we present a novel approach for providing an adaptive bit-rate control for the Region-of-Interest (ROI) Scalable Video Coding (SVC). Recently, much of the attention in the field of video adaptation has been directed to the SVC, which is the extension of the H.264/AVC standard. The need for the scalability mainly arises from the need for spatial formats, bit rates or power. To fulfill these requirements, it would be beneficial to simultaneously transmit or store video in variety of spatial/temporal resolutions and qualities, leading to the video bit-stream scalability. The ROI coding is a desirable feature in future SVC applications. For those SVC applications, users at the decoder side usually wish to receive a high-quality decoded video stream, containing the desired ROI, which should be adaptively selected from the pre-encoded scalable bit-stream. By implementing the proposed adaptive bit-rate control for the ROI SVC, smaller quantization parameters can be used, while obtaining the same compression rate, especially, in order to optimally employ the decoder computational resources and in order to achieve optimal video presentation quality. As a result, the SVC visual presentation quality at the decoder side will be significantly improved even for the decoders with very limited computational resources, which can be very useful for various mobile devices, such as cellular phones. Further, the proposed adaptive bit-rate control scheme is especially useful for future Internet and 4G applications with limited computational resources and/or with a limited channel bandwidth, such as video conferencing (between two or more mobile device users), video transrating, video transcoding between video coding standards, and many other applications.

Efficient adaptive bit-rate control for Scalable Video Coding by using Computational Complexity-Rate-Distortion analysis

We present an adaptive pre-processing (pre-filtering) method and system for the efficient Scalable Video Coding (SVC). Our adaptive SVC pre-filtering system enables to decrease motion activity, and in turn, enables to decrease quantization fluctuations within the background region of each layer of a SVC video sequence, which contains two or more layers. In turn, this enables to use smaller quantization parameters, but still to obtain the same compression rate for the optimal usage of encoder and decoder computational resources in order to achieve optimal video presentation quality. Our adaptive pre-filtering system is very efficient since it is based on a SVC Computational Complexity-Rate-Distortion (C-R-D) analysis, adding a complexity dimension to the conventional rate-distortion (R-D) analysis for the SVC coding. As a result, the SVC visual presentation quality at the decoder side is significantly improved even for the decoders with very limited computational resources, which can be very useful for various mobile devices, such as cellular phones. Also, we perform Region-of-Interest (ROI) SVC pre-processing for enabling providing efficient coding of the desired Region-of-Interest. The performance of the presented systems is demonstrated and compared with the Joint Scalable Video Model 9.19 (JSVM 9.19) reference software.

Dynamically adjustable and scalable ROI video coding

Regions-of-interest (ROI) coding is a desirable feature in future applications of Scalable Video Coding (SVC), which is the extension of the H.264/AVC standard. For those SVC applications, users at the decoder side usually wish to receive a high-quality decoded video stream, containing the desired ROI, which should be adaptively selected from the pre-encoded scalable bit-stream. In this work, we present a novel improved dynamically adjustable and scalable ROI video coding scheme, enabling to adaptively set desirable ROI location, size, resolution and bit-rate, according to the network bandwidth and predefined settings. This, in turn, will enable providing an effective rate control for multiple ROIs, thereby enabling adaptively selecting the required ROI from multiple ROIs in the scalable bit-stream, and adaptively changing ROI spatial resolution, ROI visual quality or amount of bits allocated for each ROI, according to the bandwidth and users settings (i.e., according to the users display resolution, etc.).

Complexity-aware adaptive spatial pre-processing for ROI scalable video coding with dynamic transition region

We present a complexity-aware adaptive spatial preprocessing (pre-filtering) scheme for the efficient Region-of-Interest (ROI) Scalable Video Coding (SVC). According to the proposed approach, we adaptively vary various parameters of the SVC pre-filters, such as standard deviations, a number of filters for the dynamic pre-processing of a transition region between the ROI and background, etc., thereby enabling to dynamically adjust the desired SVC settings. In addition, our adaptive spatial pre-filtering system 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 R-D 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 spatial ROI SVC pre-processing 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.

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

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 pre-processing 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.

Theoretical approach to CMOS APS PSF and MTF modeling - evaluation

In this work, a fully theoretical CMOS active pixel sensor (APS) modulation transfer function model is formulated, evaluated, and compared with practical results. The model is based on a two-dimensional diffusion equation solution and covers the symmetrical photocarriers diffusion effect together with the impact of the pixel active area geometrical shape. Thorough scanning results obtained by means of a unique submicron scanning system (the S-cube system) from various APS chips, implemented in a standard CMOS 0.35-/spl mu/m technology, are compared with our theoretical predictions. The agreement of the presented comparison results indicates that for any potential active area shape, an analytical reliable estimate of image performance is possible.

Live video streaming with adaptive pre-processing by using scalable video coding

In this work, a novel live scalable video streaming scheme, with adaptive pre-processing (pre-filtering), is presented. The scheme employs Scalable Video Coding (SVC), which is an extension of the H.264/AVC. An adaptive pre-filter is provided for each SVC layer, while each pre-filters parameters, such as the standard deviation and kernel matrix size, are dynamically adjusted according to the varying network conditions, thereby enabling to continuously obtain an optimal visual presentation quality at the decoder end. The performance of the presented live scalable video streaming scheme is evaluated and tested in detail, thereby demonstrating significant improvements of more than 2dB.

Recent trends in online mutimedia education for heterogeneous end-user devices based on Scalable Video Coding

This paper overviews the most recent trends and research directions with regard to presenting Region-of-Interest (ROI) multimedia content by using Scalable Video Coding (SVC), which can be especially useful for online multimedia education services in the heterogeneous end-user environment. The advent of a plurality of cheaper and more powerful devices, having the ability to play, create, and transmit video content, has led to a dramatic increase in the multimedia content distribution, especially for engineering educational purposes on both wireline (e.g., cable) and wireless networks. Also, the reduction of cost of digital video cameras along with the development of user-generated content video sites (e.g., YouTube™) stimulated a new user-generated video content sector and made very strong demands for the high-quality and low-delay video communication, especially for online education courses and lectures in the engineering field. In addition, much of the attention in the field of video/multimedia adaptation is currently directed to the SVC, which is an extension of the H.264/AVC video standard. A major requirement for the SVC is to enable encoding of a high-quality video stream that contains one or more subset video streams, each of which can have for example, various temporal and spatial resolutions (i.e., QCIF, CIF, 720p, 1080p) to be presented on heterogeneous user devices, such as mobile/cellular phones, laptops, personal computers, TV sets, and the like. In this work, we focus on online multimedia engineering education by employing Region-of-Interest SVC, thereby enabling to provide an ultimate solution for streaming educational multimedia content to a plurality of heterogeneous end-user devices.