Quantization Step Size Research Articles

Colored Image Compression Using Gradient Adjustment Prediction Based Wavelet

Problem statement: Uncompressed graphics, audio and video data require considerable storage capacity and transmission bandwidth. Despite rapid progress in mass-storage density, processor speeds and digital communication system performance, demand for data storage capacity and data transmission bandwidth continues to outstrip the capabilities of available technologies. The recent growth of data intensive digital audio, image and video (multimedia) based web applications, have not only sustained the need for more efficient ways to encode signals and images but have made compression of such signals central to signal-storage and digital communication technology. Approach: The objective includes developing and applying an efficient Space-Frequency Segmentation (SFS) as an image partitioning scheme, then using an appropriate entropy-coding algorithm that can be used with the developed segmentation to improve compression performance, particularly in the case of still image compression. The proposed compression system focuses on an innovative scheme for adaptive wavelet coding technique combined with spatial encoding. Result: Experiments conducted using the proposed system produced encouraging results. The entropy- spatial coders used in the proposed system produced better results than those obtained by using the basic arithmetic coder. It provides more appropriate rate-distortion optimization for the space- frequency segmentation than the basic arithmetic coder does. The proposed compression system implies some control coding parameters; the effects of these parameters were investigated to determine the suitable range for each one of them. Conclusion: We conclude that a comparison between the energy of two partitioning types (space and frequency) shows that the energy of frequency partitioning is greater than the space partitioning from the point of view of quality of compressed image. And also the selection of parameter value used in SFS part (Ratio and Threshold) should be chosen correctly, because its value has a great effect on the compressed image quality. And the selection of the quantization step size and the quantization factor (weight) should be done carefully, because experimental results show that these two factors have a great effect on the compression ratio and PSNR factor. Finally experimental results indicate that the use of GAP predictor produces better PSNR than other predictors.

A Discrete-Time Input $\Delta\Sigma$ ADC Architecture Using a Dual-VCO-Based Integrator

This brief presents a hybrid analog-digital ΔΣ analog-to-digital converter architecture with a pseudodifferential integrator based on current-controlled oscillators (CCOs). Two oscillators driven by differential input signals and followed by digital counters are used to form a quantizer whose output is digitally integrated. This pseudodifferential circuit configuration reduces the relative quantization step size and also improves linearity by canceling even-order distortion. Instead of driving a CCO with a continuous-time current input or a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> - <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> converter, a double-sampled switched-capacitor circuit is used to dump charge packets proportional to the signal voltage into the input of each oscillator. A hybrid analog-digital modulator allows for further signal processing in the digital domain to produce a low-resolution output suitable for a feedback digital-to-analog converter and an all-digital excess-loop-delay compensation feedback path. Simulation results show a signal-to-noise-plus-distortion ratio of 83.9 dB in a 2-MHz bandwidth.

Behavior of the Quantization Operator for Bandlimited, Nonoversampled Signals

The process of quantization generates a loss of information, and, thus, the original signal cannot be reconstructed exactly from the quantized samples in general. However, it is desirable to keep the error as small as possible. In this paper, the quantization error is quantified in terms of several distortion measures. All these measures employ the difference between the original signal and the reconstructed signal, which is obtained by bandlimited interpolation of the quantized samples. We assume that the signals are bandlimited and that the samples are taken at Nyquist rate. It is shown that for signals in the Paley-Wiener space PW ? 1, the supremum of the reconstructed signal, and, hence, the quantization error cannot be bounded in the sense that there exists a bounded subset of PW ? 1 on which both quantities can increase unboundedly. This unexpected behavior is due to the nonlinearity of the quantization operator and the slow decay of the sinc function. The nonlinearity is essential for this behavior because every linear operator that fulfills a certain property of the quantization operator would otherwise have to be bounded. Furthermore, it is proven that for a fixed signal the possible quantization error increases as the quantization step size tends to zero. The treatment of the quantization error in this paper is completely deterministic.

ROI-centered compression by adaptive quantization for sports video

In this paper, we propose a region of interest (ROI)-centered compression technique by adaptive quantization for sports videos. The loss of visual detail in an ROI in a broadcast soccer video is solved as seen in DMB (Digital Multimedia Broadcasting) through the mobile device. For tracking an ROI, in a soccer video, we track ball and player candidates using the Dynamic Kalman Filter (DKF) and send information of ROI macro-blocks (MBs) determined by tracking results to an encoder. The encoder allocates quantization step size (Qp) to ROI and non-ROI MBs based on priority and controls bit-rate using proposed rate-control (RC) algorithm. Experiment results show the proposed method increased visual detail in the ROI while maintaining a similar bit rate with the uniform quantization method.

Adaptive-Compression Based Congestion Control Technique for Wireless Sensor Networks

Congestion in a wireless sensor network causes an increase in the amount of data loss and delays in data transmission. In this paper, we propose a new congestion control technique (ACT, Adaptive Compression-based congestion control Technique) based on an adaptive compression scheme for packet reduction in case of congestion. The compression techniques used in the ACT are Discrete Wavelet Transform (DWT), Adaptive Differential Pulse Code Modulation (ADPCM), and Run-Length Coding (RLC). The ACT first transforms the data from the time domain to the frequency domain, reduces the range of data by using ADPCM, and then reduces the number of packets with the help of RLC before transferring the data to the source node. It introduces the DWT for priority-based congestion control because the DWT classifies the data into four groups with different frequencies. The ACT assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a smaller number of packets for a data group with a low priority. In the relaying node, the ACT reduces the amount of packets by increasing the quantization step size of ADPCM in case of congestion. Moreover, in order to facilitate the back pressure, the queue is controlled adaptively according to the congestion state. We experimentally demonstrate that the ACT increases the network efficiency and guarantees fairness to sensor nodes, as compared with the existing methods. Moreover, it exhibits a very high ratio of the available data in the sink.

Rate-distortion optimization of scalable video codecs

In this paper joint optimization of layers in the layered video coding is investigated. Through theoretical analysis and simulations, it is shown that, due to higher interactions between the layers in a SNR scalable codec, this type of layering technique benefits most from joint optimization of the layers. A method for joint optimization is then proposed, and its compression efficiency is contrasted against the separate optimization and an optimized single layer coder. It is shown that, in joint optimization of SNR scalable coders when the quantization step size of the enhancement layer is larger than half the step size of the base layer, an additional improvement is gained by not sending the enhancement zero valued quantized coefficients, provided they are quantized at the base-layer. This will result in a non-standard bitstream syntax and as an alternative for standard syntax, one may skip the inter coded enhancement macroblocks. Through extensive tests it is shown that while separate optimization of SNR coders is inferior to single layer coder by more than 2 dB, with joint optimization this gap is reduced to 0.3–0.5 dB. We have shown that through joint optimization quality of the base layer video is also improved over the separate optimization. It is also shown that spatial scalability like SNR scalability does benefit from joint optimization, though not being able to exploit the relation between the quantizer step sizes. The amount of improvement depends on the interpolation artifacts of upsampled base-layer and the residual quantization distortion of this layer. Hence, the degree of improvement depends on image contents as well as the bit rate budget. Simulation results show that joint optimization of spatial scalable coders is about 0.5–1 dB inferior to the single layer optimized coder, where its separate optimization counterpart like SNR scalability is more than 2 dB worse.

Complete Characterization of Stable Bandlimited Systems Under Quantization and Thresholding

In this paper, we analyze the approximation behavior of sampling series, where the sample values-taken equidistantly at Nyquist rate-are disturbed either by the nonlinear threshold operator or the nonlinear quantization operator. We perform the analysis for several spaces of bandlimited signals and completely characterize the spaces for which an approximation is possible. Additionally, we study the approximation of outputs of stable linear time-invariant systems by sampling series with disturbed samples for signals in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PW</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pi</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We show that there exist stable systems that become unstable under thresholding and quantization and that the approximation error is unbounded irrespective of how small the quantization step size is chosen. Further, we give a necessary and sufficient condition for the pointwise and the uniform convergence of the series. Surprisingly, this condition is the well-known condition for bounded-input bounded-output (BIBO) stability. Finally, we discuss the special case of finite-impulse-response (FIR) filters and give an upper bound for the approximation error.

A Fine Rate Control Algorithm With Adaptive Rounding Offsets (ARO)

Rate control plays an important role in regulating the bit rate to meet the bandwidth and storage requirement. Most existing video encoders regulate the bit rate by adjusting the quantization step size. We propose to incorporate a new dimension: the quantization rounding offset into rate control. In this paper, we present a rate control algorithm with adaptive rounding offsets (ARO) that jointly adjusts the quantization step size and the rounding offset for high bit rate accuracy. Different from the quantization step size that has a limited number of choices, the rounding offset is a continuously adjustable variable that allows the rate control algorithm to reach any precision in principle. Our extensive experimental results show that the proposed ARO algorithm significantly improves the rate control accuracy at almost no extra computational complexity. Compared with the rho-domain rate control, the ARO algorithm reduces the rate control errors from about 2% to 0.5% for INTRA frames, and 5% to 1.5% for INTER frames. Our experiments also demonstrate that ARO provides the extra benefit of smoother visual quality.

Model-Based Transrating of H.264 Coded Video

This paper presents a model-based transrating (bit-rate reduction) system for H.264 coded video via requantization. In works related to previous standards, optimal requantization step sizes were obtained via Lagrangian optimization that minimizes the distortion subject to a rate constraint. Due to H.264 advanced coding features, the choices of quantization step size and coding modes are dependent and the rate control becomes computationally expensive. Therefore, optimal requantization algorithms developed for previous standards cannot be applied as they are. Hence, previous works on transrating in H.264 focused on changing the input coding decisions rather than on rate control, while requantization was addressed by a simple one-pass algorithm. Here we propose new model-based optimal requantization algorithms for transrating of H.264 coded video. The optimal requantization goal is to achieve the target bit rate with minimal effect on video quality. Incorporation of the proposed models serves two goals. For intra-coded frames, a novel closed-loop statistical estimator that overcomes spatial neighbors dependencies is developed. For inter-coded frames, the proposed macroblock-level models reduce the computational burden of the optimization. Overall, as compared to re-encoding (cascaded decoder-encoder), the proposed system reduces the computational complexity by a factor of about four, at an average PSNR loss of only 0.4 dB for transrating CIF/SIF sequences from 2 Mb/s to 1 Mb/s. In comparison with a simple one-pass requantization, the proposed algorithm achieves better performance (an average PSNR gain of 0.45 dB), at the cost of just twice the complexity.

Joint Optimization of Run-Length Coding, Huffman Coding, and Quantization Table With Complete Baseline JPEG Decoder Compatibility

To maximize rate distortion performance while remaining faithful to the JPEG syntax, the joint optimization of the Huffman tables, quantization step sizes, and DCT indices of a JPEG encoder is investigated. Given Huffman tables and quantization step sizes, an efficient graph-based algorithm is first proposed to find the optimal DCT indices in the form of run-size pairs. Based on this graph-based algorithm, an iterative algorithm is then presented to jointly optimize run-length coding, Huffman coding, and quantization table selection. The proposed iterative algorithm not only results in a compressed bitstream completely compatible with existing JPEG and MPEG decoders, but is also computationally efficient. Furthermore, when tested over standard test images, it achieves the best JPEG compression results, to the extent that its own JPEG compression performance even exceeds the quoted PSNR results of some state-of-the-art wavelet-based image coders such as Shapiro's embedded zerotree wavelet algorithm at the common bit rates under comparison. Both the graph-based algorithm and the iterative algorithm can be applied to application areas such as web image acceleration, digital camera image compression, MPEG frame optimization, and transcoding, etc.

Transcoding Method of H.264 Coded Bitstream for Interest Region

We propose a transcoding method of H.264 coded bitsteam to control the picture quality dependently on the interest region. In the proposed method, first we find the model of quantization step-size and bitrate. Then a classification method according to the subjectively interest region within a video sequence is suggested. Also we propose a method that assigns a specific quantization step-size differentially according to the interest region within a video. In general, the subjective picture quality can be increased by applying the quantization step-size as a small value relatively for the interest region compared with the other regions. From the simulation, we show that the proposed method can make better subjective picture quality relatively in parts of interest region.

Soft Decision Quantization for H.264 With Main Profile Compatibility

In this paper, we study the rate-distortion (RD) optimization of the H.264 main profile encoding. Specifically, a soft decision quantization (SDQ) algorithm is developed based on the context adaptive binary arithmetic coding (CABAC) method in the H.264 main profile. Given motion prediction and quantization step sizes, the proposed SDQ algorithm is proved to achieve near-optimal SDQ for residual coding in the sense of minimizing the true RD cost when the weak adjacent block dependency utilized in CABAC is ignored for optimization. The SDQ algorithm is then used in conjunction with a general RD optimization framework to jointly design motion prediction and residual coding for H.264 main profile coding given previously coded reference frames. Experiments have been conducted based on the reference encoder JM82 of H.264 main profile. Comparative studies show that the joint design method achieves on average 10% rate reduction at the same PSNR when compared with the RD method in the H.264 main-profile reference software, with half of the reduction coming from the proposed SDQ algorithm, and 20% rate reduction at the same PSNR when compared with the RD method in the H.264 baseline-profile reference software.

OPTIMAL RATE ALLOCATION FOR LOGO WATERMARKING

Compression of logo as an image is addressed in logo watermarking, in which a framework of joint Human Vision System (HVS) model and rate allocation theory in the wavelet domain is applied. Under this framework, a novel logo watermarking algorithm using reversible discrete wavelet transform is proposed. Based on multi-level wavelet decomposition of both host and logo images, a well-known HVS model is applied to locate the visually insensitive area in the host for embedding, while the rate allocation theory determines how the logo is compressed and embedded by using the statistical characteristics of the logo. For a given overall embedding distortion, quantization step-sizes of different logo subbands are analytically determined to maximize the fidelity of the extracted logo under the imperceptibility constraint. The adaptive system is thus applicable to different hosts and logos without tuning the parameters manually. It is proved to be robust against various types of attacks and is quite suitable for hardware implementations. Since logo is considered as an image in this new algorithm, the watermarking approach developed can be extended in general to image-in-image embedding.

Improving Robustness of Quantization-Based Image Watermarking via Adaptive Receiver

In this paper, the watermarking channel is modeled as a generalized channel with fading and nonzero mean additive noise. In order to improve the watermark robustness against the generalized channel, we present an optimized watermark extraction scheme by using an adaptive receiver for quantization-based watermarking. In the proposed extraction scheme, we adaptively estimate the decision zone of the binary data bits and the quantization step size. A training sequence is embedded into the original image together with the informative watermark. The estimation of the decision zone takes advantage of the response function of the training sequence. Compared to those watermarking schemes without receiver adaptation, the main improvement is the enhanced robustness against median filtering, image intensity Direct Current (DC) change, histogram equalization, color reduction, image intensity linear scaling, image intensity nonlinear scaling such as Gamma correction etc.

Locally Adaptive Perceptual Compression for Color Images

The main idea in perceptual image compression is to remove the perceptual redundancy for representing images at the lowest possible bit rate without introducing perceivable distortion. A certain amount of perceptual redundancy is inherent in the color image since human eyes are not perfect sensors for discriminating small differences in color signals. Effectively exploiting the perceptual redundancy will help to improve the coding efficiency of compressing color images. In this paper, a locally adaptive perceptual compression scheme for color images is proposed. The scheme is based on the design of an adaptive quantizer for compressing color images with the nearly lossless visual quality at a low bit rate. An effective way to achieve the nearly lossless visual quality is to shape the quantization error as a part of perceptual redundancy while compressing color images. This method is to control the adaptive quantization stage by the perceptual redundancy of the color image. In this paper, the perceptual redundancy in the form of the noise detection threshold associated with each coefficient in each subband of three color components of the color image is derived based on the finding of perceptually indistinguishable regions of color stimuli in the uniform color space and various masking effects of human visual perception. The quantizer step size for the target coefficient in each color component is adaptively adjusted by the associated noise detection threshold to make sure that the resulting quantization error is not perceivable. Simulation results show that the compression performance of the proposed scheme using the adaptively coefficient-wise quantization is better than that using the band-wise quantization. The nearly lossless visual quality of the reconstructed image can be achieved by the proposed scheme at lower entropy.

H.264/AVC-based multiple description video coding using dynamic slice groups

In this paper, an H.264/AVC-based multiple description video coding scheme is proposed. It utilizes the advanced video coding tools and features provided in H.264/AVC to introduce redundancy into descriptions. Two independently decodable descriptions are generated, each consisting of two slice groups. One of them, called main slice group (MSG), is encoded normally as main information. The other one, called side slice group (SSG), is encoded with fewer bits as redundancy information by using larger quantization step sizes. Spatial and temporal correlations between neighboring macroblocks in video frames are exploited to achieve efficient redundancy coding. Experimental results show that the proposed MDC scheme is superior to previous slice group based multiple description coding (MDC) schemes in terms of the rate-distortion (R-D) performance.

FPGA Implementation of Integer Transform and Quantizer for H.264 Encoder

This paper deals with the process of Transformation and Quantization that is carried out on each inter-predicted residual block in a video encoding process and their reduced complexity hardware implementation. H.264/AVC utilizes 4?×?4 integer transform, which is derived from the 4?×?4 DCT. We propose, a reduced complexity algorithm and a pipelined structure for the Core forward integer transform module. A multiplier-less architecture is realized with less number of shifts and adds compared to existing works. The corresponding inverse transform is exactly reversible. Each of the transformed coefficients is quantized by a scalar quantizer. The quantization step size can be varied from macroblock to macroblock. The proposed unified pipelined architecture outperforms many recent implementations in terms of gate count and is capable of processing a 4?×?4 residual block in 4 clock cycles.

An improved scalar Costa scheme based on Watson perceptual model

An improved scalar Costa scheme (SCS) was proposed by using improved Watson perceptual model to adaptively decide quantization step size and scaling factor. The improved scheme equals to embed hiding data based on an actual image. In order to withstand amplitude scaling attack, the Watson perceptual model was redefined, and the improved scheme using the new definition can insure quantization step size in decoder that is proportional to amplitude scaling attack factor. The performance of the improved scheme outperforms that of SCS with fixed quantization step size. The improved scheme combines information theory and visual model.

Adaptive Pre-Processing Algorithm to Improve Coding Performance of Seriously Degraded Video Sequences for H.264 Video Coder

This paper introduces an adaptive low complexity pre-processing filter to improve the coding performance of seriously degraded video sequences that is caused by the additive noise. The additive noise leads to a decrease in coding performance due to the high frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given quantization step size. In order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.

A Reset State Estimator for Linear Systems to Suppress Sensor Quantization Effects

This paper presents a reset state estimator to improve the position estimation for motion control systems with sensor quantization. The reset scheme is guided by the idea that the actual output is known exactly to be at the mid-point of the two consecutive quantizer levels and is within the range of a quantizer level bounded by half of quantization step size. Hence, using this information to update the estimated state can give a better estimation under the influence of disturbance and quantization noise. We also show that the reset scheme will not destroy the stability of a baseline estimator system. The reset state estimator is applied to a linear motor control system with an optical encoder. Simulation and experiment demonstrate that the reset state estimator can achieve smaller position estimation error and more accurate tracking accuracy than those of a standard state estimator.