State-of-the-art digital multimedia platforms contain a powerful Very-Long Instruction Word (VLIW) processing core. To obtain optimum performance on such a platform, a media-processing algorithm should exploit all the advantages provided by the VLIW processor's architecture. This paper focuses on a method for adapting a video compression algorithm for implementation on a VLIW processor using algorithm-architecture co-design. We demonstrate the importance of our adaptation approach and show the improved efficiency of the algorithm implementation.

Summary form only given. We recently proposed elastic storage, a new solution for storing a variable number of images in a fixed storage space. Initially, when only a few images are stored, they are stored losslessly (or near-losslessly). When more images have to be added, the storage space occupied by the previously stored images is adjusted to fit in each new image. A variable amount of space is allocated to each image, depending on its complexity. The implementation is based on bit-rate scalable image compression methods (such as JPEG 2000), which enable the data for each new image to simply overwrite part of the data of previously stored images. Thus, the method operates without any decoding and re-encoding of image data on the storage medium. The new strategy is implemented by a simple modification of the compressed image data block significance values used for the elastic storage method. Therefore, all basic operations (store, retrieve, delete) remain the same and the low complexity of the original strategy implementation is retained.

Summary form only given. We present a method that extends lossless compression with the feature of fine-granularity scalable near lossless compression while preserving the high compression efficiency and low complexity exhibited by dedicated lossless compression methods when compared to the scalable compression methods developed for lossy image compression. The method operates by splitting the image pixel values into their most significant bits (MSB) and least significant bits (LSB). The MSB are losslessly compressed by a dedicated lossless compression method (e.g. JPEG-LS). The LSB are compressed by a scalable encoder, i.e. in such a way that their description may be truncated at any desired point. We also present a method to automatically and adaptively determine the MSB/LSB split point such that a scalable bit string is obtained without affecting the compression efficiency and without producing compression artefacts for near-lossless compression. To determine the split point, first a low complexity DPCM-type prediction is carried out on the original pixel values to obtain the prediction error signal. Next, the split point is computed from the average value of the magnitude of this signal. Finally, applying a (lossless) color transform to decorrelate the image color components before compressing them provides a higher (lossless) compression ratio.

We present a new solution for storing a variable number of images in a fixed storage space at the highest overall quality. Initially, when only a few images are stored, they are stored losslessly (or near-losslessly). When more images have to be added, the storage space occupied by the previously stored images is adjusted optimally, in the rate-distortion sense, to fit in each new image. Thus, a variable amount of space is allocated to each image, depending on its complexity. The implementation is based on bit-rate scalable image compression methods, which enables the data for each new image to simply overwrite part of the data of previously stored images. Thus, the method operates without any decoding and re-encoding of image data on the storage medium. To illustrate the concept, we present the results of our experiments for a digital image camera application.

We have developed a scalable image compression scheme with a good performance-complexity trade-off. Like JPEG, it is based on the 8/spl times/8 block discrete cosine transform (DCT), but it uses no additional quantization or entropy coding (such as Huffman or arithmetic coding). Bit-rate or quality scalability is enabled by encoding the DCT coefficients bit plane by bit plane, starting at the most significant plane. The individual bit planes are efficiently encoded using simple rectangular zones. Our method offers about the same compression performance as JPEG, but at a significant lower complexity and with the additional feature of scaling the bit rate by simply truncating the generated bit string.

We have developed a scalable image compression scheme with a good performance-complexity trade-off. Like JPEG, it is based on the 8/spl times/8 block discrete cosine transform (DCT), but it uses no additional quantization or entropy coding (such as Huffman or arithmetic coding). Bit-rate or quality scalability is enabled by encoding the DCT coefficients bit plane by bit plane, starting at the most significant plane. The individual bit planes are efficiently encoded using simple rectangular zones. Our method offers about the same compression performance as JPEG, but at a lower complexity and with the additional feature of scaling the bit rate by simply truncating the generated bit string.

Summary form only given. Arithmetic coding is a well-known technique for lossless coding or data compression. We have developed two new multiplication-free methods. Our first new method is to round A to x bits instead of truncating it. Rounding is equivalent to truncating A to its x most significant bits if the (x+1)th most significant bit of A is a 0 and adding 1 to the truncated representation if the (x+1)th most significant bit is a 1. The rounding that is applied in our new method increases the complexity (compared to truncation), since, in about half of the cases, 1 has to be added to the truncated representation. As an alternative, we therefore developed a second new method, which we call "partial rounding". By partial rounding we mean that 1 is only added to the truncated representation of A in the case when the (x+1)th most significant bit is a 1 and the xth most significant bit is a 0. In the implementation this means that the xth bit of the approximation of A equals the logical OR of the xth and (x+l)th most significant bits of the original A. The partial rounding of this second new method results in the same approximation as the "full rounding" of the first method in about 75% of the cases, but its complexity is as low as that of truncation (since the complexity of the OR is negligible). Applying the various multiplication-free methods in the arithmetic coder has demonstrated that our new rounding-based method outperforms the previously published multiplication-free methods. The "partial rounding" method outperforms the previously published truncation-based methods.

A binary data format, known as direct stream digital (DSD), has been proposed for the representation of audio signals. The typical sample rate of these binary audio signals is 64 x 44.1 kHz, so the bit rate equals 64 x 44100 = 2.8 Mbit/s (per audio channel). In order to reduce this rate, without affecting the audio quality in any way, lossless compression of these signals is necessary. Because the well-known general compression methods for binary data are too complex to be practically applied to these signals, we have developed a new lossless compression method that exploits only linear dependencies in the signal, which allows a real-time hardware implementation, even for contexts (memory) of 100 bits, or more. The encoder and decoder block diagrams for our method are shown.

Previously, performance gains over Ungerboeck type trellis-coded modulation schemes were obtained by optimizing (by hand) the signal constellation. Using genetic algorithms and simulated annealing, we have found additional cases with performance gains over the Ungerboeck type schemes.

New constructions of trellis waveform coders, trellis-coded quantizers, and trellis-coded vector quantizers are proposed. The performances of the new quantizers are determined for the memoryless Laplacian, Gaussian, and uniform sources. They are better than (for the Gaussian and Laplacian sources) or equal to (for the uniform source) the best previously published results.>

Based on a unified analysis of both pure and slotted ALOHA systems, a new control algorithm for ALOHA systems is proposed. A feature of great practical importance is the algorithm's automatic adjustment to changes in average traffic intensity or the (finite or infinite) number of active stations in the system. In addition, the algorithm has a low-complexity implementation. Computer simulations, concentrating on the use for two-way data communication in a cable television network, have demonstrated that the practical performance of the algorithm closely approximates the theoretical optimum, even under extremely heavy traffic load conditions. Furthermore, dynamic performance simulations have shown that the algorithm assures swift recovery from overload situations.>