Entropy Decoding Research Articles

Fast entropy decoding method for high resolution personal video recorder

In this paper, we propose a fast entropy decoding method that is well suited for personal video recorder (PVR) applications for high resolution video (HRV). In PVR applications supporting HRV, it is essential to implement a fast encoder and decoder due to the large amount of video data. The proposed bit-precision based entropy method (BPEM) provides a way to design a very simple entropy decoding process, which can substantially reduce the overall computational complexity of the decoder. For the purpose of contents protection, it is common to have a proprietary codec in PVR. In such a case, additional transcoding for storing transmitted data is required. However, this may involve a complex process that requires encoding and decoding. The proposed method is only applied to the entropy coding method of the existing standard codecs for transcoding thereby reducing the transcoding complexity down to bitstream-level symbol mapping. We implemented BPEM on an MPEG-4 simple profile (SP) codec. The experimental results showed that BPEM produced about 30 percent lower decoder complexity and 50 to 80 percent lower syntax parsing time while maintaining similar coding efficiency compared to the conventional MPEG-4 SP codec.

Maximum entropy decoding of multivariate neural spike trains

Scalable algorithms for decoding the information content of trains of action potentials fired by ensembles of neurons are of increasing interest for several reasons. Firstly, neurophysiological recording approaches such as multielectrode arrays (MEAs) and two-photon calcium imaging are now producing recordings from unprecedented numbers and densities of neurons simultaneously. Secondly, recent work in the area of brain-machine interfaces has opened up the opportunity of taking advantage of such algorithms for purposes such as prosthetic device control, perceptual readout and communication with tetraplegic and locked-in syndrome patients. One approach taken for decoding multivariate neural spike trains is Bayesian decoding (see e.g. [1]), where the stimulus s is decoded via the rule s = argmaxs {P*(r|s)P(s)/P*(r)}, where P* refers to a model response probability function fit using a dataset. In the present work, we extend this approach to maximum entropy models of neural population response patterns [2,3]. To preserve decoder temporal resolution, we decode spatial patterns of spikes from time windows short enough that the responses of each cell can be considered binary. We use a training dataset to measure trial-averaged nth order correlations, then apply a numerical optimization algorithm to compute online the likelihood of observing each response pattern in the test dataset. We have examined the cross-validated performance of the algorithm by decoding patterns of activity in a 2D Ising Model in which stimulus dependent statistical structure has been imposed at different orders (see Figure 1). In addition to studying simulated data, we demonfrom Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

Statistical Framework for Video Decoding Complexity Modeling and Prediction

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time-feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding.

Robust dual watermarking algorithm for AVS video

In this paper, we propose a robust dual watermarking algorithm for video based on the audio video coding standard (AVS). One watermark is embedded in luminance components, whose embedding positions are optimized using the particle swarm optimization (PSO) technique, resisting common signal processing operations such as median filtering, adding noises, cropping and so on. To combat geometric and re-encoding attacks, the other watermark is embedded in chrominance components by adjusting the energy relationship between Cb and Cr components based on the just noticeable distortion (JND) concept of the human vision system (HVS). Both of them are implemented in the compressed domain by modifying the quantized coefficients, combined together to satisfy the requirements of robustness and imperceptibility. Only entropy decoding is required to detect the watermark, and thus the watermark detection process has low computational complexity. Extensive experimental results show that the high robustness and good quality of watermarked AVS video can be achieved simultaneously.

Improved BSDL-based content adaptation for JPEG 2000 and HD Photo (JPEG XR)

JPEG 2000 and HD Photo (JPEG XR) enable the coding of images with several adaptivity provisions. These provisions allow taking into account the constraints of diverse usage environments. However, the actual adaptation of the coded bitstreams requires additional system complexity. This paper investigates how JPEG 2000 and HD Photo can be used in a standardized and XML-based framework for format-independent content adaptation in the compressed domain. An analysis is provided of the cost of providing adaptivity in the compressed domain, in terms of loss of coding efficiency, as well as in terms of the complexity of the format-agnostic adaptation system used. This complexity is expressed in terms of execution times, memory consumption, and file size overhead of the XML descriptions. Using the outcome of our analysis, a number of improvements are proposed that allow reducing the complexity of XML-based adaptation for JPEG 2000 and HD Photo, in particular the use of compact XML descriptions and an adaptation chain based on the Simple API for XML (SAX). Compact XML descriptions contain a minimal amount of information for the purpose of adaptation—their use is enabled by introducing an additional lightweight pre- and post-processing step in the overall adaptation chain. Our results show that editing-style operations on JPEG 2000 and HD Photo bitstreams can be executed with a feasible complexity when relying on compact XML descriptions. These editing-style operations include exploiting spatial and quality scalability, as well as interactive extraction of regions-of-interest (ROIs). However, low-level operations in the compressed domain, such as rotating an image, cannot be supported, due to the need for entropy decoding and data reordering at macroblock or codeblock level.

Memory Efficient Hierarchical Lookup Tables for Mass Arbitrary-Side Growing Huffman Trees Decoding

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper addresses the optimization problem of minimizing the number of memory access subject to a rate constraint for any Huffman decoding of various standard codecs. We propose a Lagrangian multiplier based penalty-resource metric to be the targeting cost function. To the best of our knowledge, there is few related discussion, in the literature, on providing a criterion to judge the approaches of entropy decoding under resource constraint. The existing approaches which dealt with the decoding of the single-side growing Huffman tree may not be memory-efficient for arbitrary-side growing Huffman trees adopted in current codecs. By grouping the common prefix part of a Huffman tree, in stead of the commonly used single-side growing Huffman tree, we provide a memory efficient hierarchical lookup table to speed up the Huffman decoding. Simulation results show that the proposed hierarchical table outperforms previous methods. A Viterbi-like algorithm is also proposed to efficiently find the optimal hierarchical table. More importantly, the Viterbi-like algorithm obtains the same results as that of the brute-force search algorithm. </para>

Reduced-Reference Quality Assessment for JPEG-2000 Compressed Image

Image quality assessment method is a methodology that measures the difference of quality between the reference image and its distorted one. In this paper, we propose a novel reduced-reference (RR) quality assessment method for JPEG-2000 compressed images, which exploits the statistical characteristics of context information extracted through partial entropy decoding or decoding. These statistical features obtained in the process of JPEG-2000 encoding are transmitted to the receiver as side information and used to estimate the quality of images transmitted over various noisy channels at the decompression side. In the framework of JPEG-2000, the context of a current coefficient is determined depending on the pattern of the significance and/or the sign of its neighbors in three bit-plane coding passes and four coding modes. As the context information represents the local property of images, it can efficiently describe textured pattern and edge orientation. The quality of transmitted images is measured by the difference of entropy of context information between received and original images. Moreover, the proposed quality assessment method can directly process the images in the JPEG-2000 compressed domain without full decompression. Therefore, our proposed can accelerate the work of assessing image quality. Through simulations, we demonstrate that our method achieves fairly good performance in terms of the quality measurement accuracy as well as the computational complexity.

Complexity Model Based Proactive Dynamic Voltage Scaling for Video Decoding Systems

Significant power savings can be achieved on voltage/ frequency configurable platforms by dynamically adapting the frequency and voltage according to the workload (complexity). Video decoding is one of the most complex tasks performed on such systems due to its computationally demanding operations like inverse filtering, interpolation, motion compensation and entropy decoding. Dynamically adapting the frequency and voltage for video decoding is attractive due to the time-varying workload and because the utility of decoding a frame is dependent only on decoding the frame before the display deadline. Our contribution in this paper is twofold. First, we adopt a complexity model that explicitly considers the video compression and platform specifics to accurately predict execution times. Second, based on this complexity model, we propose a dynamic voltage scaling algorithm that changes effective deadlines of frame decoding jobs. We pose our problem as a buffer-constrained optimization and show that significant improvements can be achieved over the state-of-the-art dynamic voltage scaling techniques without any performance degradation. Index

Adaptive Linear Prediction for Resource Estimation of Video Decoding

Current systems often assume "worst case" resource utilization for the design and implementation of compression techniques and standards, thereby neglecting the fact that multimedia coding algorithms require time-varying resources, which differ significantly from the "worst case" requirements. To enable adaptive resource management for multimedia systems, resource-estimation mechanisms are needed. Previous research demonstrated that online adaptive linear prediction techniques typically exhibit superior efficiency to other alternatives for resource prediction of multimedia systems. In this paper, we formulate the problem of adaptive linear prediction of video decoding resources by analytically expressing the possible adaptation parameters for a broad class of video decoders. The resources are measured in terms of the time required for a particular operation of each decoding unit (e.g., motion compensation or entropy decoding of a video frame). Unlike prior research that mainly focuses on estimation of execution time based on previous measurements (i.e., based on autoregressive prediction or platform and decoder-specific off-line training), we propose the use of generic complexity metrics (GCMs) as the input for the adaptive predictor. GCMs represent the number of times the basic building blocks are executed by the decoder and depend on the source characteristics, decoding bit rate, and the specific algorithm implementation. Different GCM granularities (e.g., per video frame or macroblock) are explored. Our previous research indicated that GCMs can be measured or modeled at the encoder or the video server side and they can be streamed to the decoder along with the compressed bitstream. A comparison of GCM-based versus autoregressive adaptive prediction over a large range of adaptation parameters is performed. Our results indicate that GCM-based prediction is significantly superior to the autoregressive approach and also requires less computational resources at the decoder. As a result, a novel resource-prediction tradeoff is explored between: 1) the communication overhead for GCMs and/or the implementation overhead for the realization of the predictor and 2) the improvement of the prediction performance. Since this tradeoff can be significant for the decoder platform (either from the communication or the implementation perspective), we propose complexity (or communication)-bounded adaptive linear prediction in order to derive the best resource estimation under the given implementation (or GCM-communication) bound

A 50% Power Reduction in H.264/AVC HDTV Video Decoder LSI by Dynamic Voltage Scaling in Elastic Pipeline

We propose an elastic pipeline that can apply dynamic voltage scaling (DVS) to hardwired logic circuits. In order to demonstrate its feasibility, a hardwired H.264/AVC HDTV decoder is designed as a real-time application. An entropy decoding process is divided into context-based adaptive binary arithmetic coding (CABAC) and syntax element decoding (SED), which has advantages of smoothing workload for CABAC and keeping efficiency of the elastic pipeline. An operating frequency and supply voltage are dynamically modulated every slot depending on workload of H.264 decoding to minimize power. We optimize the number of slots per frame to enhance power reduction. The proposed decoder achieves a power reduction of 50% in a 90-nm process technology, compared to the conventional clock-gating scheme.

DESIGN AND IMPLEMENTATION OF WAVELET-DOMAIN VIDEO COMPRESSION USING MULTIRESOLUTION MOTION ESTIMATION AND COMPENSATION

Video compression techniques have been applied routinely in order to conserve storage space and minimize bandwidth utilization in various video applications. To reduce inter-pixel redundancies inside and between video frames, video codecs (coder/decoder or compression/decompression) are mainly characterized by applying motion estimation and motion compensation (MEMC) in combination with discrete cosine transform (DCT). However, blocking artifacts are obvious from the block-based motion estimation and transformation, especially at low bit rates. Due to the intrinsic advantages of multiresolution and scalability of discrete wavelet transform (DWT), video compression techniques have been infused with exciting prospects by the beneficial integration of motion estimation and wavelet transformation. The contributions of this paper are in (1) proposing a design architecture of the wavelet-domain encoder and decoder; (2) implementing the multiresolution motion estimation and compensation method; and in (3) realizing the dynamic entropy encoding and decoding, so as to achieve more efficient video compression than the conventional spatial domain methods for low bit rate applications without the annoying blocking artifacts.

An Area-Efficient Variable Length Decoder IP Core Design for MPEG-$hbox 1/2/4$Video Coding Applications

This paper proposes an area-efficient variable length decoder (VLD) IP core design for MPEG- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$hbox 1/2/4$</tex> video coding applications. The proposed IP core exploits the parallel numerical matching in the MPEG- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$hbox 1/2/4$</tex> entropy decoding to achieve high data throughput rate in terms of limited hardware cost. This feature not only improves the performance of VLD, but also facilitates reducing the power consumption through lowering down the supply voltage while maintaining enough data throughput rate. Moreover, we propose a partial combinational component enabling approach for minimizing the power consumption of the proposed design. Based on 0.18- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$mu$</tex> m CMOS technology, the implementation results show that the proposed IP core operates at 125-MHz clock frequency with the cost of 13 105 gates. In addition, the power consumption of the proposed design reaches 163.4 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$mu$</tex> W operated at 12.5 MHz with 0.9-V supply voltage, which is fast enough for MPEG-1/2/4 real-time decoding on 4CIF video@30 Hz. Compared to the existing designs, the proposed IP core possesses both higher data throughput and less hardware cost.

Design Framework For JPEG2000 System Architecture

Abstract For the exploration of system architecture dedicated to JPEG2000 coding, decoding and codec, a novel design framework is constructed. In order to utilize the scalability of JPEG2000 algorithm aggressively in system implementation, three types of modules are prepared for JPEG2000 coding/decoding/codec processes, i.e. softwaze, software accelerated with user-defined instructions, and dedicated hardware. Specifically, dedicated hazdware modules for forward and inverse discrete wavelet transformation (shortly DWT), entropy coder, entropy decoder, and entropy codec as well as software acceleration for the DWT process are devised to be used in the framework. Furthermore, a JPEG2000 encoder LSI, which consists of a configurable processor Xtensa, the DWT module, and the entropy coder, is fabricated to exemplify the system implementation designed through the use of proposed framework.

Application Specific Processor Design for H.264 Decoder with a Configurable Embedded Processor

An application specific processor for an H.264 decoder with a configurable embedded processor is designed in this research. The motion compensation, inverse integer transform, inverse quantization, and entropy decoding algorithm of H.264 decoder software are optimized. We improved the performance of the processor with instruction-level hardware optimization, which is tailored to configurable embedded processor architecture. The optimized instructions for video processing can be used in other video compression standards such as MPEG 1, 2, and 4. A significant performance improvement is achieved with high flexibility. Experimental results show that we could achieve 300% performance for the H.264 baseline profile level 2 decoder.

A Software-Hardware Co-Implementation of MPEG-4 Advanced Video Coding (AVC) Decoder with Block Level Pipelining

We present a baseline MPEG-4 Advanced Video Coding (AVC) decoder based on the methodology of joint optimization of software and hardware. The software is first optimized with algorithm improvements for frame buffer management, boundary padding, content-aware inverse transform and context-based entropy decoding. The overall decoding throughput is further enhanced by pipelining the software and the dedicated hardware at macroblock level. The decoder is partitioned into the software and hardware modules according to the target frame rate and complexity profiles. The hardware acceleration modules include motion compensation, inverse transform and loop filtering. By comparing the optimized decoder with the committee reference decoder of Joint Video Team (JVT), the experimental results show improvement on the decoding throughput by 7 to 8 times. On an ARM966 board, the optimized software without hardware acceleration can achieve a decoding rate up to 5.9 frames per second (fps) for QCIF video source. The overall throughput is improved by another 27% to 7.4 fps on the average and up to 11.5 fps for slow motion video sequences. Finally, we provide a theoretical analysis of the ideal performance of the proposed decoder.

Pel reconstruction on FPGA-augmented TriMedia

This paper presents a TriMedia processor extended with three reconfigurable designs for entropy decoding (ED), inverse quantization (IQ), and two-dimensional (2-D) inverse discrete cosine transform (IDCT), and assesses the performance gain that is provided by such extensions when performing MPEG2-compliant pel reconstruction. We first describe an extension of the TriMedia architecture, which consists of a multiple-context field programmable gate array (FPGA)-based reconfigurable functional unit (RFU), a configuration unit managing the reconfiguration of the RFU, and their associated instructions. Then, we address the computation of the ED, IQ, and 2-D IDCT tasks, and propose to provide reconfigurable hardware support for a variable-length decoder that can decode two symbols per call (VLD-2), an inverse quantizer that can dequantize four coefficients per call (IQ-4), and an 1-D IDCT (1-D IDCT). The most important aspects concerning the implementation of the FPGA-mapped VLD-2, IQ-4, and 1-D IDCT units, as well as the organization of the software routines calling these FPGA-mapped computing units are outlined. Experimental results indicate that by configuring each of the VLD-2, IQ-4, and 1-D IDCT units on a different FPGA context, and by activating the contexts as needed, the FPGA-augmented TriMedia can perform MPEG2-compliant pel reconstruction with an average speed-up of 1.4/spl times/ over the standard TriMedia.

Retrieving digital images from a JPEG compressed image database

In this paper, we propose a new method of feature extraction in order to improve the efficiency of retrieving Joint Photographic Experts Group (JPEG) compressed images. Our feature extraction can be done directly to JPEG compressed images. We extract two features, DC feature and AC feature, from a JPEG compressed image. Then we measure the distances between the query image and the images in a database in terms of these two features. Our image retrieval system will give each retrieved image a rank to define its similarity to the query image. Furthermore, instead of fully decompressing JPEG images, our system only needs to do partial entropy decoding. Therefore, our proposed scheme can accelerate the work of retrieving images. According to our experimental results, our system is not only highly efficient but is also capable of performing satisfactorily.

Cumulative content of volume 8

With the increasing number of processor cores available in modern computing architectures, task or data parallelism is required to maximally exploit the available hardware and achieve optimal processing speed. Current state-of-the-art data-parallel processing methods for decoding image and video bitstreams are limited in parallelism by dependencies introduced by the coding tools and the number of synchronization points introduced by these dependencies, only allowing task or coarse-grain data parallelism. In particular, entropy decoding and data prediction are bottleneck coding tools for parallel image and video decoding. We propose a new data-parallel processing scheme for block-based intra sample and coefficient prediction that allows fine-grain parallelism and is suitable for integration in current and future state-of-the-art image and video codecs. Our prediction scheme enables maximum concurrency, independent of slice or tile configuration, while minimizing synchronization points. This paper describes our data-parallel processing scheme for one- and two-dimensional prediction and investigates its application to block-based image and video codecs using JPEG XR and H.264/AVC Intra as a starting point. We show how our scheme enables faster decoding than the state-of-the-art wavefront method with speedup factors of up to 21.5 and 7.9 for JPEG XR and H.264/AVC Intra coding tools respectively. Using the H.264/AVC Intra coding tool, we discuss the requirements of the algorithm and the impact on decoded image quality when these requirements are not met. Finally, we discuss the impact on coding rate in order to allow for optimal parallel intra decoding.

An entropy coding system for digital HDTV applications

Run-length coding (RLC) and variable-length coding (VLC) are widely used techniques for lossless data compression. A high-speed entropy coding system using these two techniques is considered for digital high definition television (HDTV) applications. Traditionally, VLC decoding is implemented through a tree-searching algorithm as the input bits are received serially. For HDTV applications, it is very difficult to implement a real-time VLC decoder of this kind due to the very high data rate required. A parallel structured VLC decoder which decodes each codeword in one clock cycle regardless of its length is introduced. The required clock rate of the decoder is thus lower, and parallel processing architectures become easy to adopt in the entropy coding system. The parallel entropy coder and decoder are designed for implementation in two experimental prototype chips which are designed to encode and decode more than 52 million samples/s. Some related system issues, such as the synchronization of variable-length codewords and error concealment, are also discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>