Showing papers on "Quantization (image processing) published in 2005"

Rate-distortion analysis for H.264/AVC video coding and its application to rate control

Abstract: In this paper, an efficient rate-control scheme for H.264/AVC video encoding is proposed. The redesign of the quantization scheme in H.264/AVC results in that the relationship between the quantization parameter and the true quantization stepsize is no longer linear. Based on this observation, we propose a new rate-distortion (R-D) model by utilizing the true quantization stepsize and then develop an improved rate-control scheme for the H.264/AVC encoder based on this new R-D model. In general, the current R-D optimization (RDO) mode-selection scheme in H.264/AVC test model is difficult for rate control, because rate control usually requires a predetermined set of motion vectors and coding modes to select the quantization parameter, whereas the RDO does in the different order and requires a predetermined quantization parameter to select motion vectors and coding modes. To tackle this problem, we develop a complexity-adjustable rate-control scheme based on the proposed R-D model. Briefly, the proposed scheme is a one-pass process at frame level and a partial two-pass process at macroblock level. Since the number of macroblocks with the two-pass processing can be controlled by an encoder parameter, the fully one-pass implementation is a subset of the proposed algorithm. An additional topic discussed in this paper is about video buffering. Since a hypothetical reference decoder (HRD) has been defined in H.264/AVC to guarantee that the buffers never overflow or underflow, the more accurate rate-allocation schemes are proposed to satisfy these requirements of HRD.

A Secure Image Steganography using LSB, DCT and Compression Techniques on Raw Images

Abstract: Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected. In this paper we present an image based steganography that combines Least Significant Bit(LSB), Discrete Cosine Transform(DCT), and compression techniques on raw images to enhance the security of the payload. Initially, the LSB algorithm is used to embed the payload bits into the cover image to derive the stego-image. The stego-image is transformed from spatial domain to the frequency domain using DCT. Finally quantization and runlength coding algorithms are used for compressing the stego-image to enhance its security. It is observed that secure images with low MSE and BER are transferred without using any password, in comparison with earlier works.

Perturbed quantization steganography

Abstract: In this paper, we use the recently proposed wet paper codes and introduce a new approach to passive-warden steganography called perturbed quantization. In perturbed quantization, the sender hides data while processing the cover object with an information-reducing operation that involves quantization, such as lossy compression, downsampling, or A/D conversion. The unquantized values of the processed cover object are considered as side information to confine the embedding changes to those unquantized elements whose values are close to the middle of quantization intervals. This choice of the selection channel calls for wet paper codes as they enable communication with non-shared selection channel. Heuristic is presented that indicates that the proposed method provides better steganographic security than current JPEG steganographic methods. This claim is further supported by blind steganalysis of a specific case of perturbed quantization for recompressed JPEG images.

High-dynamic-range still-image encoding in JPEG 2000

Abstract: The raw size of a high-dynamic-range (HDR) image brings about problems in storage and transmission. Many bytes are wasted in data redundancy and perceptually unimportant information. To address this problem, researchers have proposed some preliminary algorithms to compress the data, like RGBE/XYZE, OpenEXR, LogLuv, and so on. HDR images can have a dynamic range of more than four orders of magnitude while conventional 8-bit images retain only two orders of magnitude of the dynamic range. This distinction between an HDR image and a conventional image leads to difficulties in using most existing image compressors. JPEG 2000 supports up to 16-bit integer data, so it can already provide image compression for most HDR images. In this article, we propose a JPEG 2000-based lossy image compression scheme for HDR images of all dynamic ranges. We show how to fit HDR encoding into a JPEG 2000 encoder to meet the HDR encoding requirement. To achieve the goal of minimum error in the logarithm domain, we map the logarithm of each pixel value into integer values and then send the results to a JPEG 2000 encoder. Our approach is basically a wavelet-based HDR still-image encoding method.

Effects of quantization in phase-shifting digital holography

Abstract: We discuss quantization effects of hologram recording on the quality of reconstructed images in phase-shifting digital holography. We vary bit depths of phase-shifted holograms in both numerical simulation and experiments and then derived the complex amplitude, which is subjected to Fresnel transformation for the image reconstruction. The influence of bit-depth limitation in quantization has been demonstrated in a numerical simulation for spot-array patterns with linearly varying intensities and a continuous intensity object. The objects are provided with uniform and random phase modulation. In experiments, digital holograms are originally recorded at 8 bits and the bit depths are changed to deliver holograms at bit depths of 1 to 8 bits for the image reconstruction. The quality of the reconstructed images has been evaluated for the different quantization levels.

Lossless embedding of data in digital objects

Abstract: Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.), lossy image formats (JPEG, JPEG2000, wavelet), and palette formats (GIF, PNG).

Hardware testing for error tolerant multimedia compression based on linear transforms

Abstract: In this paper, we propose a system-level error tolerance scheme for systems where a linear transform is combined with quantization. These are key components in multimedia compression systems, e.g., video and image codecs. Using the concept of acceptable degradation, our scheme classifies hardware faults into acceptable and unacceptable faults. We propose analysis techniques that allow us to estimate the faults' impact on compression performance, and in particular on the quality of decoded images/video. We consider as an example the discrete cosine transform (DCT), which is part of a large number of existing image and video compression systems. We propose methods to establish thresholds of acceptable degradation and corresponding testing algorithms for DCT-based systems. Our results for a JPEG encoder using a typical DCT architecture show that over 50% of single stuck-at interconnection faults in one of its 1D DCT modules lead to imperceptible quality degradation in the decoded images, over the complete range of compression rates at which JPEG can operate.

A comparison of hardware implementations of the biorthogonal 9/7 DWT: convolution versus lifting

Abstract: The filter bank approach for computing the discrete wavelet transform (DWT), which we call the convolution method, can employ either a nonpolyphase or polyphase structure. This work compares filter banks with an alternative polyphase structure for calculating the DWT-the lifting method. We look at the traditional lifting structure and a recently proposed "flipping" structure for implementing lifting. All filter bank structures are implemented on an Altera field-programmable gate array. The quantization of the coefficients (for implementation in fixed-point hardware) plays a crucial role in the performance of all structures, affecting both image compression quality and hardware metrics. We design several quantization methods and compare the best design for each approach: the nonpolyphase filter bank, the polyphase filter bank, the lifting and flipping structures. The results indicate that for the same image compression performance, the flipping structure gives the smallest and fastest, low-power hardware.

Benchmarking steganographic and steganalysis techniques

Abstract: There have been a number of steganography embedding techniques proposed over the past few years. In turn the development of these techniques have led to an increased interest in steganalysis techniques. More specifically Universal steganalysis techniques have become more attractive since they work independently of the embedding technique. In this work, our goal is to compare a number of universal steganalysis techniques proposed in the literature which include techniques based on binary similarity measures, wavelet coefficients' statistics, and DCT based image features. These universal steganalysis techniques are tested against a number of well know embedding techniques, including Outguess, F5, Model based, and perturbed quantization. Our experiments are done using a large dataset of JPEG images, obtained by randomly crawling a set of publicly available websites. The image dataset is categorized with respect to the size and quality. We benchmark embedding rate versus detectability performances of several widely used embedding as well as universal steganalysis techniques. Furthermore, we provide a framework for benchmarking future techniques.

Evaluation of Image Similarity by Histogram Intersection

Abstract: Colour is the most widely used attribute in image retrieval and object recognition. A technique known as histogram intersection has been widely studied and is con- sidered to be effective for color-image indexing. The key issue of this algorithm is the selection of an appropriate color space and optimal quantization of the selected color space. The goal of this article is to measure the model performance in predicting human judgment in similarity measurement for various images, to explore the capability of the model with a wide set of color spaces, and to find the optimal quantization of the selected color spaces. Six color spaces and twelve quantization levels are involved in eval- uating the performance of histogram intersection. The cat- egorical judgment and rank order experiments were con- ducted to measure image similarity. The CIELAB color space was found to perform at least as good as or better than the other color spaces tested, and the ability to predict image similarity increased with the number of bins used in the histograms, for up to 512 bins (8 per channel). With more than 512 bins, further improvement was negligible for the image datasets used in this study. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 265-274, 2005; Published online in Wiley Inter-

Parallel-Beam Backprojection: An FPGA Implementation Optimized for Medical Imaging

Abstract: Medical image processing in general and computerized tomography (CT) in particular can benefit greatly from hardware acceleration. This application domain is marked by computationally intensive algorithms requiring the rapid processing of large amounts of data. To date, reconfigurable hardware has not been applied to the important area of image reconstruction. For efficient implementation and maximum speedup, fixed-point implementations are required. The associated quantization errors must be carefully balanced against the requirements of the medical community. Specifically, care must be taken so that very little error is introduced compared to floating-point implementations and the visual quality of the images is not compromised. In this paper, we present an FPGA implementation of the parallel-beam backprojection algorithm used in CT for which all of these requirements are met. We explore a number of quantization issues arising in backprojection and concentrate on minimizing error while maximizing efficiency. Our implementation shows approximately 100 times speedup over software versions of the same algorithm running on a 1 GHz Pentium, and is more flexible than an ASIC implementation. Our FPGA implementation can easily be adapted to both medical sensors with different dynamic ranges as well as tomographic scanners employed in a wider range of application areas including nondestructive evaluation and baggage inspection in airport terminals.

Block transform and quantization for image and video coding

Abstract: An improved method and block transform for image or video encoding and decoding, wherein transformation and inverse transformation matrixes are defined such that computational complexity is significantly reduced when encoding and decoding. For example, in the two-dimensional inverse transformation of de-quantized transform coefficients into output pixel information during decoding, only four additions plus one shift operation are needed, per co-efficient transformation, all in sixteen-bit arithmetic. Transformations provide correct results because quantization during encoding and de-quantization (sixteen bit) during decoding, via the use of one of three tables selected based on each coefficient's position, have parameter values that already compensate for factors of other transformation multiplications, except for those of a power of two, (e.g., two or one-half), which are performed by a shift operation during the transformation and inverse transformation processes. Computational complexity is significantly reduced with respect to other known transforms without adversely impacting compression or quality.

Compression of encrypted three-dimensional objects using digital holography

Abstract: We present the results of applying data compression techniques to encrypted three-dimensional objects. The objects are captured using phase-shift digital holography and encrypted using a random phase mask in the Fresnel domain. Lossy quantisation is combined with lossless coding techniques to quantify compression rates. Lossless compression alone applied to the encrypted holographic data achieves compression rates lower than 1.05. When combined with quantisation and an integer encoding scheme, this rises to between 12 and 65 (depending on the hologram chosen and the method of measuring compression rate) with good decryption and reconstruction quality. Our techniques are suitable for a range of secure three-dimensional object storage and transmission applications.

Robust watermark embedding/detection algorithm for H.264 video

Abstract: We present a blind watermark embedding/detection al- gorithm to embed watermarks into H.264's I pictures. The embed- ded watermark can survive H.264's compression attacks with more than a 40:1 compression ratio in I pictures. One pair of predicted discrete cosine transform (DCT) coefficients within the blocks of size 434 are used to embed 1-bit watermark information. The embed- ding locations of DCT coefficients are switched from lower sub- bands to higher subbands in a predefined order, while the distortions between predicted values and original values are larger than a tol- erable bound. With these methods, the embedded watermarks can survive H.264's compression attacks with good invisibility. © 2005

Image information conversion apparatus and image information conversion method

Abstract: The invention provides an image information conversion apparatus and method by which picture quality deterioration caused by conversion from inputted MPEG2 image compression information into MPEG4 image compression information to be outputted is prevented. When an I picture of an MPEG2 bit stream is to be converted into a P-VOP of an MPEG4 bit stream based on an estimated value of the complexity for each VOP, a scene change detection section detects whether or not the I picture includes a scene change. If a scene change is detected by the scene change detection section, then a GOV structure determination section determines that conversion of the I picture of the MPEG2 bit stream into a P-VOP of an MPEG4 bit stream should not be performed.

Using perceptual models to improve fidelity and provide invariance to valumetric scaling for quantization index modulation watermarking

Abstract: Quantization index modulation (QIM) is a computationally efficient method of watermarking with side information. This paper proposes two improvements to the original algorithm. First, the fixed quantization step size is replaced with an adaptive step size that is determined using Watson's perceptual model. Experimental results on a database of 1000 images illustrate significant improvements in both fidelity and robustness to additive white Gaussian noise. Second, modifying the Watson model such that it scales linearly with valumetric (amplitude) scaling, results in a QIM algorithm that is invariant to valumetric scaling. Experimental results compare this algorithm with both the original QIM and an adaptive QIM and demonstrate superior performance.

Video image encoding method, video image encoder, and video image encoding program

Abstract: A method for encoding a video image includes: generating a prediction image for each of a plurality of pixel blocks that are divided from an input image into a predetermined size, and generating a prediction residual signal that indicates prediction residual between the prediction image and each of the pixel blocks, for each of a plurality of prediction modes; obtaining an orthogonal transformation coefficient by performing orthogonal transformation to the prediction residual signal corresponding to each of the prediction modes; selecting a target prediction mode from among the prediction modes based on a number of the orthogonal transformation coefficients that become non-zero as a quantization processing is performed; encoding each of the pixel blocks in the target prediction mode respectively selected.

Moving picture encoding method, device using the same, and computer program

Abstract: If a reference frame displayed at the reproduction side immediately before a current image frame to be encoded is an inter encoding image frame, a quantization control device (999) appropriately corrects the level (quantization value) of an intra encoding so as to visually reduce I-frame flicker caused by a difference between the inter encoding noise characteristic of the inter encoding image frame and the noise characteristic of the current intra encoding.

Regression-based prediction for blocking artifact reduction in JPEG-compressed images

Abstract: In order to reduce the blocking artifact in the Joint Photographic Experts Group (JPEG)-compressed images, a new noniterative postprocessing algorithm is proposed. The algorithm consists of a two-step operation: low-pass filtering and then predicting. Predicting the original image from the low-pass filtered image is performed by using the predictors, which are constructed based on a broken line regression model. The constructed predictor is a generalized version of the projector onto the quantization constraint set , , or the narrow quantization constraint set . We employed different predictors depending on the frequency components in the discrete cosine transform (DCT) domain since each component has different statistical properties. Further, by using a simple classifier, we adaptively applied the predictors depending on the local variance of the DCT block. This adaptation enables an appropriate blurring depending on the smooth or detail region, and shows improved performance in terms of the average distortion and the perceptual view. For the major-edge DCT blocks, which usually suffer from the ringing artifact, the quality of fit to the regression model is usually not good. By making a modification of the regression model for such DCT blocks, we can also obtain a good perceptual view. The proposed algorithm does not employ any sophisticated edge-oriented classifiers and nonlinear filters. Compared to the previously proposed algorithms, the proposed algorithm provides comparable or better results with less computational complexity.

Motion analysis in 3D DCT domain and its application to video coding

Abstract: Global, constant-velocity, translational motion in an image sequence induces a characteristic energy footprint in the Fourier-transform (FT) domain; spectrum is limited to a plane with orientation defined by the direction of motion. By detecting these spectral occupancy planes, methods have been proposed to estimate such global motion. Since the discrete cosine transform (DCT) is a ubiquitous tool of all video compression standards to date, we investigate in this paper properties of motion in the DCT domain. We show that global, constant-velocity, translational motion in an image sequence induces in the DCT domain spectral occupancy planes, similarly to the FT domain. Unlike in the FT case, however, these planes are subject to spectral folding. Based on this analysis, we propose a motion estimation method in the DCT domain, and we show that results comparable to standard block matching can be obtained. Moreover, by realizing that significant energy in the DCT domain concentrates around a folded plane, we propose a new approach to video compression. The approach is based on 3D DCT applied to a group of frames, followed by motion-adaptive scanning of DCT coefficients (akin to “zig-zag” scanning in MPEG coders), their adaptive quantization, and final entropy coding. We discuss the design of the complete 3D DCT coder and we carry out a performance comparison of the new coder with ubiquitous hybrid coders.

ASIC and FPGA implementations of H.264 DCT and quantization blocks

Abstract: In the search for ever better and faster video compression standards H.264 was created. With it arose the need for hardware acceleration of its very computationally intensive parts. To address this need, this paper proposes two sets of architectures for the integer discrete transform (DCT) and quantization blocks from H.264. The first set of architectures for the DCT and quantization were optimized for area, which resulted in transform and quantizer blocks that occupy 294 and 1749 gates respectively. The second set of speed optimized designs has a throughput anywhere from 11 to 2552 M pixels/s. All of the designs were synthesized for Xilinx Virtex 2-Pro and 0.18/spl mu/m TSMC CMOS technology, as well as the combined DCT and quantization blocks went through comprehensive place and route flow.

Shared key encryption of JPEG color images

Abstract: Several methods have been proposed for encrypting images by shared key encryption mechanisms since the work of Naor and Shamir. All the existing techniques are applicable to primarily non-compressed images in either monochrome or color domains. However, most imaging applications including digital photography, archiving, and Internet communications nowadays use images in the JPEG compressed format. Application of the existing shared key cryptographic schemes for these images requires conversion back into spatial domain. In this paper we propose a shared key algorithm that works directly in the JPEG domain, thus enabling shared key image encryption for a variety of applications. The scheme directly works on the quantized DCT coefficients and the resulting noise-like shares are also stored in the JPEG format. The decryption process is lossless preserving the original JPEG data. The experiments indicate that each share image is approximately the same size as the original JPEG image retaining the storage advantage provided by JPEG compression standard. Three extensions, one to improve the random appearance of the generated shares, another to obtain shares with asymmetric file sizes, and the third to generalize the scheme for n>2 share cases, are described as well.

Image processing apparatus, method of controlling thereof, and program

Abstract: An object of the present invention is to detect characteristics of images such as blurring and the like without expanding compressed and stored image data. And, the above-described detection is realized by detecting image characteristics on the basis of attached information of an image, such as relation between low frequency components and high frequency components of alternate current components and contents of a quantization table of image data compressed by a compression system for converting image data into spatial frequency components. In addition, for example, as a result of detection, an image discriminated to be in a blurred state is controlled so as not to be inserted into an album template or to be inserted into a small area inside the album template. Thereby, a user can omit work of picking and choosing images to insert into an album template while taking a look at a lot of images.

Advanced Performance Achievement using Multi- Algorithmic Approach of Video Transcoder for Low Bitrate Wireless Communication

Abstract: This paper presents the different issue of video streaming algorithms without compromising the video quality in the distributed environment. Our theme of this research is to manage the critical processing stages (speed, information loss, redundancy and error resilience) having better encoded ratio, without the fluctuation of quantization scale by using IP configuration. In this paper, we implement different techniques such as upgraded features of the Motion Compensation with Discrete Cosine Transform (MCDCT) domain criteria regarded with prediction error and Motion Vector (MV) at low-frequency coefficient, while control of frame resolution having downscale of future prediction error with lower transmission bitrate in this streaming media. However, delay of bits in encoded buffer side is being controlled to produce the video with high quality and maintenance a low buffering delay. Our results show the performance accuracy gain with better achievement than a number of previous approaches in all the above processes in an encouraging mode.

Region-sensitive compression of digital video

Abstract: A video coding method for surveillance videos allowing some regions of the scene to be encoded in an almost lossless manner. Such Regions of Interest (RoI) can be determined a priori or they can be automatically determined in real-time by an intelligent system. The user can set high priority in such regions a priori or the intelligent video analysis algorithm can automatically assign some windows a higher priority compared to the rest of the video. In a preferred embodiment, this can be achieved by canceling the motion estimation and compensation operations, and then decreasing the size of the quantization levels during the encoding process in the RoI. The present inventions can produce MPEG compatible bit-streams without sending any side information specifying the RoI.

Evaluation of image compression algorithms for fingerprint and face recognition systems

Abstract: A variety of widely accepted and efficient compression methods do exist for still images. To name a few, there are standardised schemes like JPEG and JPEG2000 which are well suited for photorealistic true colour and grey scale images and usually operated in lossy mode to achieve high compression ratios. These schemes are well suited for images that are processed within face recognition systems. In the case of forensic biometric systems, compression of fingerprint images has already been applied in automatic fingerprint identification systems (AFIS) applications, where the size of the digital fingerprint archives would be tremendous for uncompressed images. In these large scale applications wavelet scalar quantization has a long tradition as an effective encoding scheme. This paper gives an overview of the study BioCompress that has been conducted at Fraunhofer IGD on behalf of the Federal Office for Information Security (BSI). Based on fingerprint and face image databases and different biometric algorithms we evaluated the impact of lossy compression algorithms on the recognition performance of biometric recognition systems.

Multi-pass video encoding

Abstract: Some embodiments of the invention provide a multi-pass encoding method that encodes several images (e.g., several frames of a video sequence). The method iteratively performs an encoding operation that encodes these images (Figure 1, 110). The encoding operation is based on a nominal quantization parameter, which the method uses to compute quantization parameters for the images (132). During several different iterations of the encoding operation, the method uses several different nominal quantization parameters (125). The method stops its iterations (140) when it reaches a terminating criterion (e.g., it identifies an acceptable encoding of the images).

Lossy compression of images with additive noise

Abstract: Lossy compression of noise-free and noisy images differs from each other. While in the first case image quality is decreasing with an increase of compression ratio, in the second case coding image quality evaluated with respect to a noise-free image can be improved for some range of compression ratios. This paper is devoted to the problem of lossy compression of noisy images that can take place, e.g., in compression of remote sensing data. The efficiency of several approaches to this problem is studied. Image pre-filtering is shown to be expedient for coded image quality improvement and/or increase of compression ratio. Some recommendations on how to set the compression ratio to provide quasioptimal quality of coded images are given. A novel DCT-based image compression method is briefly described and its performance is compared to JPEG and JPEG2000 with application to lossy noisy image coding.