Andrew G. Tescher

A comparative study of JPEG2000, AVC/H.264, and HD photo

In this paper, we report a study evaluating rate-distortion performance between JPEG 2000, AVC/H.264 High 4:4:4 Intra and HD Photo. A set of ten high definition color images with different spatial resolutions has been used. Both the PSNR and the perceptual MSSIM index were considered as distortion metrics. Results show that, for the material used to carry out the experiments, the overall performance, in terms of compression efficiency, are quite comparable for the three coding approaches, within an average range of ±10% in bitrate variations, and outperforming the conventional JPEG.

Near-lossless bandwidth compression for radiometric data

We present a bandwidth compression scheme suitable for transmission of radiometric data collected bytodays sensitive and high-resolution sensors. Specific design constraints associated with this application are requirements for (1) near-lossless coding, (2) handling of a high dynamic range, and (3) placement of an upper bound on maximum coding error, as opposed to the average or rms coding error. In this approach both the spectral and spatial correlations in the data are exploited to reduce its bandwidth. Spectral correlation is first removed via the Karhunen-Loeve (KL) transformation. An adaptive discrete cosine transform coding technique is then applied to the resulting spectrally decorrelated data. Because the actual coding is done in the transform domain, each individual coding error spreads over an entire block of data when reconstructed. This helps to reduce significantly the maximum error and, as such, makes this approach very suitable for this application. A useful by-product of this approach is that it readily provides some feature classification capability, such as cloud typing, through the interpretation of KL-transformed images. Since each KL-transformed image is a linear combination of all the spectral images, it represents a blend of information present in the entire spectral image set. As such, it could solely render some useful information not readily detectable from the ensemble of spectral images. This may be of particular utility for situations in which a photo interpreter may not have the time or the opportunity to inspect the entire set of images.

Practical transform coding of multispectral imagery

In this paper we present a robust and implementable compression algorithm for multispectral imagery with a selectable quality level within the near-lossless to visually lossy range. The three-dimensional terrain-adaptive transform-based algorithm involves a one dimensional Karhunen-Loeve transform (KLT) followed by two-dimensional discrete cosine transform (DCT). The images are spectrally decorrelated via the KLT to produce the eigen images. The resulting spectrally decorrelated eigen images are then compressed using the JPEG algorithm. The key feature of this approach is that it incorporates the best methods available to fully exploit the spectral and spatial correlation in the data. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral decorrelation transformation based upon variations in the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a totally different coder (e.g., DPCM). However, the significant practical advantage of this approach is that it is leveraged on the standard and highly developed JPEG compression technology. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near- lossless at about 5:1 compression ratio (CR) to visually lossy beginning at around 40:1 CR.

Adaptive two-stage Karhunen-Loeve-transform scheme for spectral decorrelation in hyperspectral bandwidth compression

A computationally efficient adaptive two-stage Karhunen-Loeve transform (KLT) scheme for spectral decorrelation in hyperspectral lossy bandwidth compression is presented. The component decorrelation of the JPEG 2000 (extension 2) is replaced with an adaptive two-stage KLT scheme. The data are partitioned into small subsets. The spectral correlation within each partition is removed via a first-stage KLT. The interpartition spectral correlation is removed using a second-stage KLT applied to the resulting top few sets of equilevel principal component (PC) images. Since only a fraction of each equilevel first-stage PC images are used in the second stage, the KLT transformation matrices will have smaller sizes, leading to further improvement in computational complexity and coding efficiency. The computation of the proposed approach is parametrically quantified. It is shown that reconstructed image quality, as measured via statistical and/or machine-based exploitation measures, is improved by using a smaller partition size in the first-stage KLT. A criterion based on the components of the eigenvectors of the cross-covariance matrix is established to select first-stage PC images, which are used in the second-stage KLT. The proposed scheme also reduces the overhead bits required to transmit the covariance information to the receiver in conjunction with the coding bitstream.

Channel Rate Equalization Techniques For Adaptive Transform Coders

The channel rate equalization problem inherent in a variable rate coding problem is analysed in this paper. Specific solutions are developed for adaptive transform coding algorithms. The actual algorithms depend on either pretransform or post-transform buffering. Simulations indicate small performance variations between the techniques.

Adaptive Transform Coding Based on Chain Coding Concepts

Chain coding technique, originally developed for digital representation and processing of line drawing data, has been implemented in a transform image coding algorithm with significant performance improvement. The algorithm is based on the observation that the boundary of the regions of zero coefficients within a transform block can be efficiently represented by sequences of fixed line segments (chains). Preliminary results indicate significant improvements over the basic coder algorithm in which the consecutive zeros in the transform block were runlength coded. The additional implementation complexity is modest.

An Investigation Of MSE Contributions In Transform Image Coding Schemes

The mean square error (MSE) is a classical measure of image distortion. However, this metric is generally not a faithful indication of subjective image quality. We attempt to correct this deficiency by addressing the individual error sources in transform image coding. Specifically, the component of the MSE introduced by transform coefficient deletion is separated from requantization effects. Results are demonstrated in both numerical and pictorial form.

KLT/JPEG 2000 multispectral bandwidth compression with region-of-interest prioritization capability

The region of interest (ROI) coding feature of JPEG 2000 image compression standard is extended to multispectral imagery. This is accomplished by enabling ROI capability of JPEG 2000 module in the previously developed Karhunen-Loeve/JPEG 2000 compression of multispectral images. Preliminary results, based on subjective, statistical, and machine-based exploitation measures, show significant improvement in the compression performance. Depending on the ROI/background relative size and the desired quality differential, the improvement in the classification accuracy can increase by as much as one hundred percent without an increase in the bandwidth.

An adaptive two-stage KLT scheme for spectral decorrelation in hyperspectral bandwidth compression

A computationally efficient adaptive 2-stage Karhunen-Loeve Transform (KLT) scheme for spectral decorrelation in hyperspectal lossy bandwidth compression is presented. The component decorrelation of the JPEG 2000 (extension 2) is replaced with the proposed adaptive 2-stage KLT spectral decorrelation scheme. Direct application of a single KLT across the entire set of hyperspectal imagery may not be computationally practical. The proposed scheme alleviates this problem by partitioning the spectral data set into small subsets. The spectral correlation within each partition is removed via the 1st-stage KLT operation. To remove the remaining inter-partition correlation, a 2nd-stage KLT is applied to the top few sets of eaui-level principal component (PC) images from the 1st-stage. The computation savings resulting from 2-stage KLT is parametrically quantified. The proposed adaptive 2-stage KLT uses only a fraction of the equi-level 1st-stage PC images in the 2nd-stage KLT process. This adaptive scheme results in reducing the size of the 2nd-stage KLT transformation matrices and further improvement in computational complexity and coding efficiency. It is shown that reconstructed image quality, as measured via statistical and/or machine-based exploitation measures, is improved by using a smaller partition size in the 1st-stage KLT. A criterion based on the components of the eigenvectors of the cross-covariance matrix is established to identify such 1st-stage PC images. The proposed adaptive spectral decorrelation scheme also reduces the overhead bits required to transmit the covariance matrices, or eigenvectors, along the coding bit stream to the receiver through the downlink channel.

Spectral-signature-preserving compression of multispectral data

An enhancement to a previously developed Karhunen-Loeve/ discrete cosine transform-based multispectral bandwidth compression technique (Saghri et al., 1995) is presented. This enhancement is achieved via the addition of a spectral screening module prior to the spectral decorrelation process. The objective of the spectral screening module is to identify a set of unique spectral signatures in a block of multispectral data to be used in the subsequent spectral decorrelation module. The number of unique signatures found depends on the desired spectral angle separation, irrespective of their frequency of occurrence. This set of unique spectral signatures, instead of the signature of each and every point in the block of data, is used to construct the spectral covariance matrix and the resulting Karhunen-Loeve spectral transfor- mation matrix that is used to spectrally decorrelate the multispectral im- ages. The significance of this modification is that the covariance matrix so constructed is not entirely based on the statistical significance of the individual spectra in the block but rather on the uniqueness of the indi- vidual spectra. Without this added spectral screening feature, small ob- jects and ground features would likely be manifested in the low eigen- planes mixed with all of the noise present in the scene. Since these lower eigenplanes are coded via the subsequent Joint Photographic Ex- perts Group (JPEG) compression module at a much lower bit rate, the fidelity of these small objects is severely impacted by compression- induced error. However, the addition of the proposed spectral screening module relegates these small objects into the higher eigenplanes and hence greatly enhances the preservation of their fidelities in the com- pression process. This modification alleviates the need to update the covariance matrix frequently over small subblocks, resulting in a reduced overhead bit requirement and a much simpler implementation task.