Showing papers on "Discrete cosine transform published in 1995"

Manipulation and compositing of MC-DCT compressed video

Abstract: Many advanced video applications require manipulations of compressed video signals. Popular video manipulation functions include overlap (opaque or semitransparent), translation, scaling, linear filtering, rotation, and pixel multiplication. We propose algorithms to manipulate compressed video in the compressed domain. Specifically, we focus on compression algorithms using the discrete cosine transform (DCT) with or without motion compensation (MC). Such compression systems include JPEG, motion JPEG, MPEG, and H.261. We derive a complete set of algorithms for all aforementioned manipulation functions in the transform domain, in which video signals are represented by quantized transform coefficients. Due to a much lower data rate and the elimination of decompression/compression conversion, the transform-domain approach has great potential in reducing the computational complexity. The actual computational speedup depends on the specific manipulation functions and the compression characteristics of the input video, such as the compression rate and the nonzero motion vector percentage. The proposed techniques can be applied to general orthogonal transforms, such as the discrete trigonometric transform. For compression systems incorporating MC (such as MPEG), we propose a new decoding algorithm to reconstruct the video in the transform domain and then perform the desired manipulations in the transform domain. The same technique can be applied to efficient video transcoding (e.g., from MPEG to JPEG) with minimal decoding. >

Video parsing and browsing using compressed data

Abstract: Parsing video content is an important first step in the video indexing process. This paper presents algorithms to automate the video parsing task, including partitioning a source video into clips and classifying those clips according to camera operations, using compressed video data. We have developed two algorithms and a hybrid approach to partitioning video data compressed according to the JPEG and MPEG standards. The algorithms utilize both the video content encoded in DCT (Discrete Cosine Transform) coefficients and the motion vectors between frames. The hybrid approach integrates the two algorithms and incorporates multi-pass strategies and motion analyses to improve both accuracy and processing speed. Also, we present content-based video browsing tools which utilize the information, particularly about the shot boundaries and key frames, obtained from parsing.

Shape-adaptive DCT for generic coding of video

Abstract: A low complexity shape-adaptive DCT algorithm suitable for coding pels in arbitrarily shaped image segments is introduced. In contrast to other techniques described in literature the proposed algorithm is based on predefined orthogonal sets of DCT basis functions and does not require more computations than a normal block DCT. It is shown that the shape-adaptive DCT algorithm can be easily incorporated into existing block-based JPEG, H.261, or MPEG coding schemes. Thus segment or object based coding of images and video can be provided with backward compatibility to existing coding standards. As an important feature, with the proposed technique additional content based functionalities currently discussed in the MPEG-4 standardization phase can be readily achieved. >

An optimization approach for removing blocking effects in transform coding

Abstract: One drawback of the discrete cosine transform (DCT) is visible block boundaries due to coarse quantization of the coefficients. Most restoration techniques for the removing blocking effect are variations of low-pass filtering, and as such, result in unnecessary blurring. The authors propose a new approach for reducing the blocking effect which can be applied to conventional transform coding without introducing additional information or significant blurring. The method exploits the correlation between the intensity values of boundary pixels of two neighboring blocks. It is based on the theoretical and empirical observation that under mild assumptions, quantization of the DCT coefficients of two neighboring blocks increases the expected value of the mean squared difference of slope (MSDS) between the slope across two adjacent blocks, and the average between the boundary slopes of each of the two blocks. The amount of this increase is dependent upon the width of quantization intervals of the transform coefficients. Therefore, among all permissible inverse quantized coefficients, the set which reduces the expected value of this MSDS by an appropriate amount is most likely to decrease the blocking effect. To estimate the set of unquantized coefficients, the authors solve a constrained quadratic programming problem. The approach is based on the gradient projection method. It is shown that from a subjective viewpoint, the blocking effect is less noticeable in the author' processed images than in the ones using existing filtering techniques. >

On the modeling of DCT and subband image data for compression

Abstract: Image subband and discrete cosine transform coefficients are modeled for efficient quantization and noiseless coding. Quantizers and codes are selected based on Laplacian, fixed generalized Gaussian, and adaptive generalized Gaussian models. The quantizers and codes based on the adaptive generalized Gaussian models are always superior in mean-squared error distortion performance but, generally, by no more than 0.08 bit/pixel, compared with the much simpler Laplacian model-based quantizers and noiseless codes. This provides strong motivation for the selection of pyramid codes for transform and subband image coding. >

Transform-domain adaptive filters: an analytical approach

Abstract: Transform-domain adaptive filters refer to LMS filters whose inputs are preprocessed with a unitary data-independent transformation followed by a power normalization stage. The transformation is typically chosen to be the discrete Fourier transform (DFT), although other transformations, such as the cosine transform (DCT), the Hartley transform (DHT), or the Walsh-Hadamard transform, have also been proposed in the literature. The resulting algorithms are generally called DFT-LMS, DCT-LMS, etc. This preprocessing improves the eigenvalue distribution of the input autocorrelation matrix of the LMS filter and, as a consequence, ameliorates its convergence speed. In this paper, we start with a brief intuitive explanation of transform-domain algorithms. We then analyze the effects of the preprocessing performed in DFT-LMS and DCT-LMS for first-order Markov inputs. In particular, we show that for Markov-1 inputs of correlation parameter /spl rho//spl isin/[0,1], the eigenvalue spread after DFT and power normalization tends to (1+/spl rho/)l(1-/spl rho/) as the size of the filter gets large, whereas after DCT and power normalization, it reduces to (1+/spl rho/). For comparison, the eigenvalue spread before transformation is asymptotically equal to (1+/spl rho/)/sup 2//(1-/spl rho/)/sup 2/. The analytical method used in the paper provides additional insight into how the algorithms work and is expected to extend to other input signal classes and other transformations. >

Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT

Abstract: Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8/spl times/8/spl times/8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNRs of the coded hyperspectral sequences exceeding 40.0 dB. >

Method and apparatus for compressing images

Abstract: A system and method is disclosed that compresses and decompresses images The compression system and method (126, 128, 130, 132) includes an encoder (130) which compresses images and stores such compressed images in a unique file format, and a decoder (110) which decompresses images The encoder (130) optimizes the encoding process to accommodate different image types with fuzzy logic methods (152) that automatically analyze and decompose a source image, classify its components, select the optimal compression method for each component, and determine the optimal parameters of the selected compression methods The encoding methods inlcude: a Reed Spline Filter (138), a discrete cosine transform (136), a differential pulse code modulator (140), and enhancement analyzer (144), an adaptive vector quantizer (134) and a channel encoder (132) to generate a plurality of data segments that contain the compressed image The plurality of data segments are layered in the compressed file (104) to optimize the decoding process The first layer allows the decoder (110) to display the compressed image as a miniature or a coarse quality full sized image, the decoder (110) then adds additional detail and sharpness to the displayed image as each new layer is received The decoder (110) uses optimal decompression methods to expand the compressed image file

Transform coded image reconstruction exploiting interblock correlation

Abstract: Transmission of still images and video over lossy packet networks presents a reconstruction problem at the decoder. Specifically, in the case of block-based transform coded images, loss of one or more packets due to network congestion or transmission errors can result in errant or entirely lost blocks in the decoded image. This article proposes a computationally efficient technique for reconstruction of lost transform coefficients at the decoder that takes advantage of the correlation between transformed blocks of the image. Lost coefficients are linearly interpolated from the same coefficients in adjacent blocks subject to a squared edge error criterion, and the resulting reconstructed coefficients minimize blocking artifacts in the image while providing visually pleasing reconstructions. The required computational expense at the decoder per reconstructed block is less than 1.2 times a non-recursive DCT, and as such this technique is useful for low power, low complexity applications that require good visual performance. >

Comparison of generalized Gaussian and Laplacian modeling in DCT image coding

Abstract: Generalized Gaussian and Laplacian source models are compared in discrete cosine transform (DCT) image coding. A difference in peak signal to noise ratio (PSNR) of at most 0.5 dB is observed for encoding different images. We also compare maximum likelihood estimation of the generalized Gaussian density parameters with a simpler method proposed by Mallat (1989). With block classification based on AC energy, the densities of the DCT coefficients are much closer to the Laplacian or even the Gaussian. >

Volume rendering of DCT-based compressed 3D scalar data

Abstract: The paper proposes a scheme to perform volume rendering from compressed scalar data. Instead of decompressing the entire data set before rendering, blocks of data are decompressed as needed. Discrete cosine transform based compression technique is used to illustrate the method. The data is partitioned into overlapping blocks to permit local rendering and allow easy parallelization. Compression by factor of 20 to 30 produces rendering virtually indistinguishable from rendering using the original uncompressed data. Speedup is obtained by making use of spatial homogeneity detected in the transform domain. Rendering time using the proposed approach is less than that of direct rendering from the entire uncompressed data. The proposed method thus offers an attractive option to reduce storage, computation, and transmission overhead of otherwise huge data sets. >

JAGUAR: a fully pipelined VLSI architecture for JPEG image compression standard

Abstract: In this paper, we describe a fully pipelined single chip VLSI architecture for implementing the JPEG baseline image compression standard. The architecture exploits the principles of pipelining and parallelism to the maximum extent in order to obtain high speed and throughput. The architecture for discrete cosine transform and the entropy encoder are based on efficient algorithms designed for high speed VLSI implementation. The entire architecture can be implemented on a single VLSI chip to yield a clock rate of about 100 MHz which would allow an input rate of 30 frames per second for 1024/spl times/1024 color images. >

Diagonalizing properties of the discrete cosine transforms

Abstract: Since its introduction in 1974 by Ahmed et al., the discrete cosine transform (DCT) has become a significant tool in many areas of digital signal processing, especially in signal compression. There exist eight types of discrete cosine transforms (DCTs). We obtain the eight types of DCTs as the complete orthonormal set of eigenvectors generated by a general form of matrices in the same way as the discrete Fourier transform (DFT) can be obtained as the eigenvectors of an arbitrary circulant matrix. These matrices can be decomposed as the sum of a symmetric Toeplitz matrix plus a Hankel or close to Hankel matrix scaled by some constant factors. We also show that all the previously proposed generating matrices for the DCTs are simply particular cases of these general matrix forms. Using these matrices, we obtain, for each DCT, a class of stationary processes verifying certain conditions with respect to which the corresponding DCT has a good asymptotic behavior in the sense that it approaches Karhunen-Loeve transform performance as the block size N tends to infinity. As a particular result, we prove that the eight types of DCTs are asymptotically optimal for all finite-order Markov processes. We finally study the decorrelating power of the DCTs, obtaining expressions that show the decorrelating behavior of each DCT with respect to any stationary processes.

An error concealment algorithm for images subject to channel errors

Abstract: We present an algorithm to conceal bit errors in still images and image sequences that are coded using the discrete cosine transform (DCT) and variable length codes (VLCs). No modification is necessary to an existing encoder, and no additional bit rate is required. The concealment algorithm is kept simple so that real-time decoding and concealment is possible. A single bit error in these images can cause a block to split into several blocks or several blocks to merge into one. This causes the DCT coefficients of all subsequent blocks to be correctly decoded but stored in the wrong location in the image. Furthermore, the DC coefficient of all subsequent blocks may be incorrect. The error concealment algorithm uses transform domain information to identify the location of the affected blocks and to correct errors. The image quality after error concealment is shown to be significantly improved. >

Apparatus and method for compressing still images

Abstract: Transforms such as the DCT are useful for image compression. One close relative of the DCT is preferred for its arithmetic simplicity. A method and apparatus is described whereby the image compression is done with no multiplications while still compatible with a JPEG (Joint Photographic Experts Group) Transform. Other enhancements are made to improve image quality.

Combined techniques of singular value decomposition and vector quantization for image coding

Abstract: The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexity. >

High-quality audio-coding at less than 64 kbit/s by using transform-domain weighted interleave vector quantization (TwinVQ)

Abstract: A new audio-coding method is proposed. This method is called transform-domain weighted interleave vector quantization (TwinVQ) and achieves high-quality reproduction at less than 64 kbit/s. The method is a transform coding using modified discrete cosine transform (MDCT). There are three novel techniques in this method: flattening of the MDCT coefficients by the spectrum of linear predictive coding (LPC) coefficients; interframe backward prediction for flattening the MDCT coefficients; and weighted interleave vector quantization. Subjective evaluation tests showed that the quality of the reproduction of TwinVQ exceeded that of an MPEG Layer II coder at the same bitrate.

Digital 3D/stereoscopic video compression technique utilizing two disparity estimates

Abstract: Efficient digital compression of 3D/stereoscopic video is achieved by a novel technique in which various views forming 3D/stereoscopic video are coded by utilizing the redundancies among the views Coding is performed in a manner compatible with existing equipment to allowing decoding of one layer of video for display on normal (ie, monoscopic) displays The motion compensated discrete cosine transform ("DCT") coding framework of existing standards such as the Motion Pictures Expert Group-Phase 2 ("MPEG-2") video standard is exploited, and when necessary extended, to result in highly efficient, yet practical, coding schemes In constrast with known techniques of encoding the two views forming stereoscopic video which rely on the use of a disparity estimate between the two views (where one of the views is the reference, coded by itself and the other is disparity compensated predicted and coded with respect to the reference view), the present techniques utilize two disparity estimates: one disparity estimate which allows forward prediction and other disparity estimate allowing backward prediction with respect to the reference view

Image resizing in the discrete cosine transform domain

Abstract: Image resizing is a task that must often be done when processing digital images. We present a novel approach to resize images by operating entirely in the discrete cosine transform (DCT) domain. We implement the lowpass filter for anti-aliasing or anti-imaging using the convolution-multiplication property of the DCT. We perform the equivalent of pixel-domain downsampling or upsampling by simple manipulation of the DCT coefficients. Our approach can be used as a standalone image resizing tool or it can be integrated into any image compression system based on a block DCT, such as JPEG, to provide an image resizing capability with relatively little additional complexity.

A fast approximate algorithm for scaling down digital images in the DCT domain

Abstract: We present a fast approximate algorithm for scaling down an image by a factor of two, when the input and output streams are both in the form of 8/spl times/8 discrete cosine transform (DCT) transform blocks, as with JPEG coded images. Roughly speaking, the algorithm requires 80% fewer operations than the naive algorithm of inverting the transform, scaling in the spatial domain, and transforming the resulting image.

Efficiency of shape-adaptive 2-D transforms for coding of arbitrarily shaped image segments

Abstract: We introduce a formula to compute an optimum 2-D shape-adaptive Karhunen-Loeve transform (KLT) suitable for coding pels in arbitrarily-shaped image segments. The efficiency of the KLT on a 2-D AR(1) process is used to benchmark two other shape-adaptive transforms described in literature. It is shown that the optimum KLT significantly outperforms the well known shape-adaptive DCT method introduced by Gilge et al. (1989) for coding Segments of arbitrary shape in intraframe coding mode. A statistical transform gain close to the Gilge-method can be achieved with a shape-adaptive DCT algorithm introduced by Sikora and Makai (see Proc. Workshop Image Anal. Image Coding, Berlin, FRG, Nov. 1993) which is implemented with much lower complexity. >

A hierarchical image transmission system in a fading channel

Abstract: This paper proposes a new mobile image transmission system based on the hierarchical modulation scheme for achieving a high quality and high speed digital image transmission in a band-limited fading channel. The proposed system uses a hierarchical QAM (quadrature amplitude modulation) scheme to give unequal transmission reliability depending on the importance of DCT (discrete cosine transform)-based compressed images. Numerical analysis shows that the proposed system is an effective high-quality and high-speed image transmission technique in a fading channel.

RD-OPT: an efficient algorithm for optimizing DCT quantization tables

Abstract: The Discrete Cosine Transform (DCT) is widely used in lossy image and video compression schemes such as JPEG and MPEG. In this paper we describe RD-OPT, an efficient algorithm for constructing DCT quantization tables with optimal rate-distortion tradeoffs for a given image. The algorithm uses DCT coefficient distribution statistics in a novel way and uses a dynamic programming strategy to produce optimal quantization tables over a wide range of rates and distortions. It can be used to compress images at any desired signal-to-noise ratio or compressed size.

Method and apparatus for improved video decompression by selection of IDCT method based on image characteristics

Abstract: A video decoding system examines a block of DCT coefficients prior to computation of an inverse discrete cosine transform (IDCT) to determine the number of nonzero coefficients. A plurality of IDCT engines are available in the video system including an IDCT engine utilizing fewer computations for a sparse picture and an IDCT engine utilizing fewer computations for a nonsparse picture. The video decoding system selects an IDCT engine to minimize the number of computations performed and to thereby reduce the computational burden of IDCT transformation.

Segmentation-based JPEG image artifacts reduction

Abstract: A method for reducing ringing and blocking artifacts in a decompressed image models an image in a relatively small area as several smooth regions separated by edges The method uses JPEG MxM pixel blocks and is compatible with JPEG decompression To reduce ringing, a block is examined for uniformity, segmented and smoothed Then, after a DCT transform, a projection is performed to guarantee that the DCT coefficients of the resulting image block will be within the initial quantization interval The resultant image is produced by an inverse DCT To reduce blocking, the method is modified to employ a large outer window for uniformity checking, segmentation and smoothing and a small inner window for DCT projection

Weighted universal transform coding: universal image compression with the Karhunen-Loeve transform

Abstract: We introduce a two-stage universal transform code for image compression. The code combines Karhunen-Loeve transform coding with weighted universal bit allocation (WUBA) in a two-stage algorithm analogous to the algorithm for weighted universal vector quantization (WUVQ). The encoder uses a collection of transform/bit allocation pairs rather than a single transform/bit allocation pair (as in JPEG) or a single transform with a variety of bit allocations (as in WUBA). We describe both an encoding algorithm for achieving optimal compression using a collection of transform/bit allocation pairs and a technique for designing locally optimal collections of transform/bit allocation pairs. We demonstrate the performance using the mean squared error distortion measure. On a sequence of combined text and gray scale images, the algorithm achieves up to a 2 dB improvement over a JPEG style coder using the discrete cosine transform (DCT) and an optimal collection of bit allocations, up to a 3 dB improvement over a JPEG style coder using the DCT and a single (optimal) bit allocation, up to 6 dB over an entropy constrained WUVQ with first- and second-stage vector dimensions equal to 16 and 4 respectively, and up to a 10 dB improvement over an entropy constrained vector quantizer (ECVQ) with a vector dimension of 4.

Discrete cosine transform in error control coding

Abstract: The authors define a new class of real-number linear block codes using the discrete cosine transform (DCT). They also show that a subclass with a BCH-like structure can be defined and, therefore, encoding/decoding algorithms for BCH codes can be applied, A (16,10) DCT code is given as an example. >

VLSI implementation of inverse discrete cosine transformer and motion compensator for MPEG2 HDTV video decoding

Abstract: An MPEG2 video decoder core dedicated to MP@HL (Main Profile at High Level) images is described with the main theme focused on an inverse discrete cosine transformer and a motion compensator. By means of various novel architectures, the inverse discrete cosine transformer achieves a high throughput, and the motion compensator performs different types of picture prediction modes employed by the MPEG2 algorithm. The decoder core, implemented in the total chip area of 22.0 mm/sup 2/ by a 0.6-/spl mu/m triple-metal CMOS technology, processes a macroblock within 3.84 /spl mu/s, and therefore is capable of decoding HDTV (1920/spl times/1152 pels) images in real time. >

Computation of discrete cosine transform using Clenshaw's recurrence formula

Abstract: Clenshaw's recurrence formula is used to derive recursive algorithms for the discrete cosine transform (DCT) and the inverse discrete cosine transform (IDCT). The recursive DCT algorithm presented requires one fewer delay element per coefficient and one fewer multiply operation per coefficient compared with two other proposed methods. Clenshaw's recurrence formula provides a unified development for the recursive DCT and IDCT algorithms. The recursive algorithms apply to arbitrary length algorithms and are appropriate for VLSI implementation. >

An efficient CORDIC array structure for the implementation of discrete cosine transform

Abstract: We propose a novel implementation of the discrete cosine transform (DCT) and the inverse DCT (IDCT) algorithms using a CORDIC (coordinate rotation digital computer)-based systolic processor array structure. First, we reformulate an N-point DCT or IDCT algorithm into a rotation formulation which makes it suitable for CORDIC processor implementation. We then propose to use a pipelined CORDIC processor as the basic building block to construct l-D and 2-D systolic-type processor arrays to speed up the DCT and IDCT computation. Due to the proposed novel rotation formulation, we achieve 100% processor utilization in both 1-D and 2-D configurations. Furthermore, we show that for the 2-D configurations, the same data processing throughput rate ran be maintained as long as the processor array dimensions are increased linearly with N. Neither the algorithm formulation or the array configuration need to be modified. Hence, the proposed parallel architecture is scalable to the problem size. These desirable features make this novel implementation compare favorably to previously proposed DCT implementations. >