Image Sequence Compression Research Articles

Contour simplification for segmented still image and video coding: algorithms and experimental results

This paper describes some new algorithms for Segmented Still Image and Image Sequence Coding. A new filter is introduced for reducing the number of contour points produced by segmentation, and also the number of bits needed to code them, leading to higher compression ratios without visible subjective degradation of the image quality. The impact of using such a contour points' reduction filter in image sequence compression is studied. The inclusion of the filter in a new video codec scheme leads to higher frame rates for almost the same subjective video quality, especially when coding complex scenes. A simple way to control the video bit-rate is also described.

Frame-Adaptive Vector Quantization

Vector quantization (VQ) is a promising technique for low bit rate image compression. Recently image sequence compression algorithms based on VQ have been reported in the literature. Image sequences are highly nonstationary and generally exhibit variations from frame to frame and from scene to scene; hence using a fixed VQ codebook to encode the different frames/sequences may not always guarantee a good coding performance. Several adaptive techniques which improve the coding performance have been reported in the literature. However, we note that most adaptive techniques result in further increases in computational complexity and/or the bit rate. In this paper, a new frame adaptive VQ technique for image sequence compression (FAVQ) is presented. This technique exploits the inter/intraframe correlations and provides frame adaptability at a reduced complexity. A dynamic self organized codebook is used to track the local statistics from frame to frame. Computer simulations using standard CCITT sequences demonstrate the superior coding performance of FAVQ. In addition, FAVQ is a single pass technique which makes possible real time implementation.

Huffman coding of DCT coefficients using dynamic codeword assignment and adaptive codebook selection

In many image sequence compression applications, Huffman coding is used to eliminate statistical redundancy resident in given data. The Huffman table is often pre-defined to reduce coding delay and table transmission overhead. Local symbol statistics, however, may be much different from the global ones manifested in the pre-defined table. In this paper, we propose three Huffman coding methods in which pre-defined codebooks are effectively manipulated according to local symbol statistics. The first proposed method dynamically modifies the symbol-codeword association without rebuilding the Huffman tree itself. The encoder and decoder maintain identical symbol-codeword association by performing the same modifications to the Huffman table, thus eliminating extra transmission overhead. The second method adaptively selects a codebook from a set of given ones, which produces the minimum number of bits. The transmission overhead in this method is the codebook selection information, which is observed to be negligible compared with the bit saving attained. Finally, we combine the two aforementioned methods to further improve compression efficiency. Experiments are carried out using five test image sequences to demonstrate the compression performance of the proposed methods.

On optimal low-rank approximation of multidimensional discrete signals

This brief describes an algorithmic development of the optimal low-rank approximation (LRA) of multidimensional (M-D) signals with M/spl ges/3. The algorithms developed can be regarded as a dimensional generalization of the singular value decomposition (SVD) which is of fundamental importance for analyzing signals that can be represented in a matrix form. In particular, iterative algorithms for optimal and suboptimal LRA of three-dimensional (3-D) arrays are presented in detail. Application of the 3-D LRA to the compression of image sequences is discussed.

Motion estimation and compensation based on almost shift‐invariant wavelet transform for image sequence coding

There has been rapid progress in the application of wavelet transforms to image and image sequence compression. The standard discrete wavelet transform lacks transition invariance in image decomposition which will affect the accuracy of motion estimation from the decomposed subimages in video coding. In this article, we present a study of applying an almost shift-invariant wavelet transform with “oversampled frames” to image sequence compression. With minimal oversampling and biorthogonal spline wavelets in the almost shift-invariant wavelet transform, motion vectors can be more accurately estimated, contributing toward fewer prediction errors in comparison to those obtained with the standard discrete wavelet transform. Thus, an improved compression ratio can be obtained. We present two new algorithms, the full-motion oversampling algorithm (FMOS) and the reduced search multiresolution motion estimation algorithm (MRME), for estimating motion fields at different scales and in different subimages. In the latter, motion vectors at a higher resolution are approximated by the motion vector estimates at a lower resolution through proper scaling. Experiments were performed on three video sequences with a variety of motions including slow, fast, and zooming. Our results have shown that both algorithms, FMOS and MRME, using the almost shift-invariant oversampled frame wavelet transform have reduced prediction errors and enhanced the compression performance in terms of peak-signal-to-noise ratio (PSNR) for the same bit rate when compared to the existing full motion standard algorithm. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 214–229, 1998

Motion estimation and compensation based on almost shift-invariant wavelet transform for image sequence coding

There has been rapid progress in the application of wavelet transforms to image and image sequence compression. The standard discrete wavelet transform lacks transition invariance in image decomposition which will affect the accuracy of motion estimation from the decomposed subimages in video coding. In this article, we present a study of applying an almost shift-invariant wavelet transform with “oversampled frames” to image sequence compression. With minimal oversampling and biorthogonal spline wavelets in the almost shift-invariant wavelet transform, motion vectors can be more accurately estimated, contributing toward fewer prediction errors in comparison to those obtained with the standard discrete wavelet transform. Thus, an improved compression ratio can be obtained. We present two new algorithms, the full-motion oversampling algorithm (FMOS) and the reduced search multiresolution motion estimation algorithm (MRME), for estimating motion fields at different scales and in different subimages. In the latter, motion vectors at a higher resolution are approximated by the motion vector estimates at a lower resolution through proper scaling. Experiments were performed on three video sequences with a variety of motions including slow, fast, and zooming. Our results have shown that both algorithms, FMOS and MRME, using the almost shift-invariant oversampled frame wavelet transform have reduced prediction errors and enhanced the compression performance in terms of peak-signal-to-noise ratio (PSNR) for the same bit rate when compared to the existing full motion standard algorithm. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 214–229, 1998

Signal compression through spatial frequency-based motion estimation

The interest in low bit rate video coding has increased considerably. Despite rapid progress in storage density and digital communication system performance, demand for data-transmission bandwidth and storage capacity continue to exceed the capabilities of available technologies. The growth of data-intensive digital audio, video applications and the increased use of bandwidth-limited media such as video conferencing and full motion video have not only sustained the need for efficient ways to encode analog signals, but made signal compression central to digital communication and data-storage technology. In this paper we explore techniques for compression of image sequences in a manner that optimizes the results for the human receiver. We propose a new motion estimator using two novel block match algorithms which are based on human perception. Simulations with image sequences have shown an improved bit rate while maintaining “image quality” when compared to conventional motion estimation techniques using the MAD block match criteria.

Motion-compensated prediction based algorithm for medical image sequence compression

A method for irreversible compression of medical image sequences is described. The method relies on discrete cosine transform and motion-compensated prediction to reduce intra- and inter-frame redundancies in medical image sequences. Simulation examples are presented.

Joint optimization of representation model and frame segmentation for generic video compression

This paper describes a new image sequence compression system capable of efficiently representing any type of visual data at very low bit-rate. The coder presents a flexible structure open to evolution. Thanks to a perfect rate and frame quality control, buffer regulation issue is solved. The technique relies on the joint optimization of two degrees of freedom, namely adaptive representation model and adaptive frame support partition. Optimization is performed in a rate-distortion sense and is based on the theory of optimal resources allocation. System performances are evaluated in a particular environment, namely at very low bit-rate in the framework of video-telephony applications. Simulation results demonstrate the efficiency of the system over bit-rates ranging from 4.6 to 32 kbit/s.

Frame adaptive finite-state vector quantization for image sequence coding

Abstract This paper presents a new adaptive method for the compression of image sequences. The method is based on a two-dimensional finite-state vector quantization (2-D FSVQ), and a frame adaptive technique using codebook and address replenishments. Each frame is described by an FSVQ super codebook and an address map. The super codebook is the collection of state codebooks and each state codebook consists of K region codebooks. The super codebook and the address map are efficiently updated on a frame basis so as to more closely match the local frame statistics, thus yielding approximately fixed quality over the entire image sequence. This method is very computationally efficient and can achieve nearly constant quality over the entire image sequence.

Adaptive Huffman coding of 2-D DCT coefficients for image sequence compression

Abstract This paper presents a new approach to adaptive Huffman coding of 2-D DCT coefficients for image sequence compression. Based on the popular motion-compensated interframe coding, the proposed method employs self-switching multiple Huffman codebooks for entropy coding of quantized transform coefficients. Unlike the existing multiple codebook approaches where the type of block (intra/inter or luminance/chrominance) selects a codebook, the proposed method jointly utilizes the type of block, the quantizer step size, and the zigzag scan position for the purpose of codebook selection. In addition, as another utilization of the quantizer step size and the scan position, the proposed method uses a variable-length “Escape” sequence for encoding rare symbols. Experimental results show that the proposed method with two codebooks provides 0.1–0.4 dB improvement over the single-codebook scheme and this margin turns out to be substantially larger than that the MPEG-2, two-codebook approach has over the single-codebook approach.

Bit-rate control for MPEG encoders

Bit-rate control is a central problem in designing image sequence compression systems. In this paper we describe a new approach to bit-rate control for inter-frame encoders such as MPEG encoders. This approach uses concepts from control theory. Its central feature is a surprisingly simple but effective model for the encoder, which consists of a gain element, a delay element and additive noise. In our system we control the bit-rate with a PI-controller which is set to achieve two objectives: (1) we want the picture quality to be as uniform as possible, and (2) we want to use as closely as possible the available amount of bits. It is demonstrated in the paper that these two objectives, when considered separately, lead to contradictory settings of the controller. This dilemma can be solved by using Bit Usage Profiles that indicate how the bits have to be spread over the pictures. The effectiveness of the approach is demonstrated by designing a bit-rate control for an MPEG encoder that has a nearly constant bit-rate per group of pictures (GOP). Such a bit-rate control is of high value for applications like magnetic recording, where a constant bit-rate per GOP is required in order to realize playback trick modes, e.g. the fast forward mode.

Image segmentation for compression of images and image sequences

Region-based image coding schemes, the so-called second generation techniques, have gained much favour in recent years. For still picture coding, they can increase the compression ratio obtained by transform coding by an order of magnitude, while maintaining adequate image representation. The success of these techniques relies on the ability to describe regions in an image succinctly by their shape and size. The algorithms presented describe methods for segmenting images. Unlike most other region based algorithms, these algorithms incorporate knowledge of the border coding process in deciding how to partition the image. The extension from single image compression to sequential image compression is also considered. A new, efficient segmentation scheme is proposed that exploits temporal redundancies between successive images, and reduces some problems associated with error accumulation in error images.

Fractal-based image sequence compression scheme

The dominant image transformation used in the existing fractal coding schemes is the affine function. Although an affine transformation is easy to compute and understand, its linear approximation ability limits the employment of larger range blocks, that is, it limits further improvement in compression efficiency. We generalize the image transformation from the usual affine form to the more general form, e.g., quadratic form, and provide theoretical requirements for the generalized transformation to be contractive. Based on the self-transformation system (STS) model, an image sequence coding scheme-fractal-based image sequence coding-is proposed. In this coding scheme, our generalized transformation is used to model the self-transformation from the domain block to its range blocks. Experimental results on a real image sequence show that for the same size of blocks, the SNR can be improved by 10 dB, or, for the same SNR of the decoded image sequence, the compression ratio is raised twofold when the new generalized transformation is used to replace the usual affine transformation. In addition, due to the utilization of the STS model, the computational complexity is only linearly related to the size of the 3-D blocks. This provides for fast encoding and decoding.

Perceptually derived localized linear operators: Application to image sequence compression

Perceptually derived localized linear operators: Application to image sequence compression

Special issue on image sequence compression IEEE transactions on image processing

Special issue on image sequence compression IEEE transactions on image processing

Contour image sequence compression through motion analysis and hybrid coding method

This paper presents a motion analysis algorithm (MAA) and a hybrid coding method for contour image sequence compression. The contour image sequence consists of objects moving and rotating in a 3-D world with occlusion, shape, and size variations from frame to frame. The MAA separates the moving image sequence into several object-oriented subsequences (OOSs). In each OOS, the component is either stationary or moves smoothly, and the motion parameters can be easily estimated. The first and last frames of OOS are key frames, and the others are in-between frames. The key frames are unpredictable, and the entire frames need to be encoded. The in-between frames are compensable, and they are encoded by the motion parameter coding. The hybrid coder uses vectorgraph coding to remove spatial redundancy of the key frames and motion parameter coding to reduce the temporal redundancy of the OOSs. The motion parameters are encoded as combinations of 2-D translation, 2-D rotation, and scaling. There are many applications for contour image sequence compression. The cartoon image sequence (a sequence of line drawing sketches) and the high-frame-rate videophone for sign language transmission are good examples. Experiments show that our method encodes the contour image sequence at a very high compression ratio without losing intelligibility.

Compression of Meteosat data for image dissemination

The high demand for Meteosat image sequences for various fields and the high rate of presentation of methods dealing with these latter is facing with the low rate of presentation of remote sensing image sequence compression methods generally for different imagers. To tackle this issue, a novel Meteosat image sequence coding method in the Radon field is presented in this paper. The idea behind this paper is to show the contribution of the coding in Radon field to an efficient lossless compression. The Radon field, represented here by a new fast Mojette transform (FMT), gives a homogeneous field through a Mojette matrix engendered by the intra-projections and inter-projections redundancy. As a consequence, the use of an efficient block-based motion estimation (ME) algorithm such as adaptive rood pattern search (ARPS) becomes possible in a such environment, which can help to avoid many problems encountered when using the same techniques for remote sensing image as a block effect by way of example. In order to achieve a high lossless compression, the Sorted Run Length Coding (SRLC) is applied to encode besides the motion vectors (MVs), the residual error represented by separate long sequences of zeros in addition to other repeated symbols. The test phase showed that our proposed scheme exceeds the performances of the JPEG-LS standard. The proposed scheme can also be used as pre-technique for methods using Meteosat images in general and their sequences in particular after the decompression process such as meteorological phenomena tracking, segmentation, and so on.

Image Sequence Compression Using a Pel-Recursive Motion-Compensated Technique

This paper presents and analyzes a pel-recursive, motioncompensated, image sequence compression algorithm [1]. The analysis retains all the terms of the Taylor's series expansion and yields a set of equations for which the convergence criteria and the convergence rate of the motion estimate are more easily seen. The existing motion prediction schemes are also reviewed and a new motion prediction scheme is presented which is shown to be superior to the existing schemes. Simulations run on actual image sequences to verify the analytical results indicate that implementing the analytical model as opposed to the generally used heuristic technique does yield a decrease in the information rate and the computational requirements. Simulation results also are included which use the projection-along-the-motiontrajectory or PAMT prediction scheme. Third, zeroth-order entropy encoding is shown to reduce the bit rate on the order of 12 percent, and to reduce the mean square error in the reconstructed images on the order of 60 percent when compared to first-order entropy encoding. Fourth, field-to-field motion prediction is compared to frame-to-frame motion prediction.