A successively refinable lossless image-coding algorithm

We present a compression technique that provides progressive transmission as well as lossless and near-lossless compression in a single framework. The proposed technique produces a bit stream that results in a progressive, and ultimately lossless, reconstruction of an image similar to what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides near-lossless reconstruction with respect to a given bound, after decoding of each layer of the successively refinable bit stream. We formulate the image data-compression problem as one of successively refining the probability density function (pdf) estimate of each pixel. Within this framework, restricting the region of support of the estimated pdf to a fixed size interval then results in near-lossless reconstruction. We address the context-selection problem, as well as pdf-estimation methods based on context data at any pass. Experimental results for both lossless and near-lossless cases indicate that the proposed compression scheme, that innovatively combines lossless, near-lossless, and progressive coding attributes, gives competitive performance in comparison with state-of-the-art compression schemes.     

An analog-to-digital converter with Golomb-Rice output codes

An analog-to-digital converter with data compression capabilities is described. By sharing circuits between an integrating converter and a Golomb-Rice encoder it is possible to jointly perform the tasks of quantization and coding. The Golomb-Rice codes are generated during the conversion cycle by employing a shift register and a digital multiplexer. The final codeword is read out serially from the shift register. The converter can also work in a noncompressing mode. This design provides a compact circuit suitable for on-sensor compression. Simulations at the system and transistor level corroborate the validity of the design.     

Compression of color-mapped images

Multispectral data is often displayed and stored as a color-mapped or pseudo-color image. Pseudo-color is also used to enhance features in a single-band image. The use of pseudo-color tends to rearrange the structure in the image in such a way as to prevent efficient compression. This structure can be restored by sorting the color maps. Restoration of the structure increases the efficiency of lossless compression and permits the use of lossy compression algorithms. The latter benefit is especially useful for many progressive transmission algorithms     

Scan predictive vector quantization of multispectral images

Conventional vector quantization (VQ)-based techniques partition an image into nonoverlapping blocks that are then raster scanned and quantized. Image blocks that contain an edge result in high-frequency vectors. The coarse representation of such vectors leads to visually annoying degradations in the reconstructed image. The authors present a solution to the edge-degradation problem based on some earlier work on scan models. The approach reduces the number of vectors with abrupt intensity variations by using an appropriate scan to partition an image into vectors. They show how their techniques can be used to enhance the performance of VQ of multispectral data sets. Comparisons with standard techniques are presented and shown to give substantial improvements.     

A joint source/channel coder with block constraints

The assumptions made about the source during source coder design result in a residual redundancy at the output of the source coder. This redundancy can be utilized for error protection without any additional channel coding. Joint source/channel coders obtained using this idea via maximum a posteriori probability decoders tend to fail at low probability of error. In this paper, we propose a modification of the standard approach which provides protection at low error rates as well     

Use of residual redundancy in the design of joint source/channelcoders

A technique for providing error protection without the additional overhead required for channel coding is presented. The authors start from the premise that, during source coder design, for the sake of simplicity or due to imperfect knowledge, assumptions have to be made about the source which are often incorrect. This results in residual redundancy at the output of the source coder. The residual redundancy can then be used to provide error protection in much the same way as the insertion of redundancy in convolutional coding provides error protection. The authors develop an approach for utilizing this redundancy. To show the validity of this approach, the authors apply it to image coding using differential pulse code modulation (DPCM), and obtain substantial performance gains, both in terms of objective and subjective measures     

A Postfiltered DPCM Scheme

Quantization noise constitutes the limiting factor on DPCM performance. For a low number of levels in the quantizer, this noise is also correlated. In this correspondence we present a technique for improving the performance of the DPCM system at a very modest cost. The technique involves the use of an adaptive transversal filter at the receiver output to reduce the effects of quantization noise. The filter is trained at the transmitter and periodic updates sent to the receiver.     

Lossless image compression with a codebook of block scans

When applying predictive compression on image data there is an implicit assumption that the image is scanned in a particular order. Clearly, depending on the image, a different scanning order may give better compression. In earlier work, we had defined the notion of a prediction tree (or scan) which defines a scanning order for an image. An image can be decorrelated by taking differences among adjacent pixels along any traversal of a scan. Given an image, an optimal scan that minimizes the absolute sum of the differences encountered can be computed efficiently. However, the number of bits required to encode an optimal scan turns out to be prohibitive for most applications. In this paper we present a prediction scheme that partitions an image into blocks and for each block selects a scan from a codebook of scans such that the resulting prediction error is minimized. Techniques based on clustering are developed for the design of a codebook of scans. Design of both semiadaptive and adaptive codebooks is considered. We also combine the new prediction scheme with an effective error modeling scheme. Implementation results are then given, which compare very favorably with the JPEG lossless compression standard     

Joint source/channel coding for variable length codes

When using entropy coding over a noisy channel, it is customary to protect the highly vulnerable bitstream with an error correcting code. In this paper, we propose a technique which utilizes the residual redundancy at the output of the source coder to provide error protection for entropy coded systems     

Lossless compression of video sequences

We investigate lossless compression schemes for video sequences. A simple adaptive prediction scheme is presented that exploits temporal correlations or spectral correlations in addition to spatial correlations. It is seen that even with motion compensation, schemes that utilize only temporal correlations do not perform significantly better than schemes that utilize only spectral correlations. Hence, we look at hybrid schemes that make use of both spectral and temporal correlations. The hybrid schemes give significant improvement in performance over other techniques. Besides prediction schemes, we also look at some simple error modeling techniques that take into account prediction errors made in spectrally and/or temporally adjacent pixels in order to efficiently encode the prediction residual. Implementation results on standard test sequences indicate that significant improvements can be obtained by the proposed techniques