Showing papers by "William A. Pearlman published in 2000"

Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)

Abstract: We propose a low bit-rate embedded video coding scheme that utilizes a 3-D extension of the set partitioning in hierarchical trees (SPIHT) algorithm which has proved so successful in still image coding. Three-dimensional spatio-temporal orientation trees coupled with powerful SPIHT sorting and refinement renders 3-D SPIHT video coder so efficient that it provides comparable performance to H.263 objectively and subjectively when operated at the bit rates of 30 to 60 kbits/s with minimal system complexity. Extension to color-embedded video coding is accomplished without explicit bit allocation, and can be used for any color plane representation. In addition to being rate scalable, the proposed video coder allows multiresolutional scalability in encoding and decoding in both time and space from one bit stream. This added functionality along with many desirable attributes, such as full embeddedness for progressive transmission, precise rate control for constant bit-rate traffic, and low complexity for possible software-only video applications, makes the proposed video coder an attractive candidate for multimedia applications.

Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm

Abstract: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm (A. Said and W.A. Pearlman, IEEE Trans. Ccts. Syst. II, vol. 6, p. 243-50, 1996) has achieved notable success in still image coding. The authors modified the algorithm for the one-dimensional case and applied it to compression of ECG data. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate.

SPIHT image compression without lists

Abstract: A variant of the SPIHT image compression algorithm called no list SPIHT (NLS) is presented. NLS operates without linked lists and is suitable for a fast, simple hardware implementation. NLS has a fixed predetermined memory requirement about 50% larger than that needed for the image alone. Instead of lists, a state table with four bits per coefficient keeps track of the set partitions and what information has been encoded. NLS sparsely marks selected descendant nodes of insignificant trees in the state table in such a way that large groups of predictably insignificant pixels are easily identified and skipped during coding passes. The image data is stored in a one dimensional recursive zig-zag array for computational efficiency and algorithmic simplicity. The performance of the algorithm on standard test images is nearly the same as SPIHT.

SBHP-a low complexity wavelet coder

Abstract: We present a low-complexity entropy coder originally designed to work in the JPEG2000 image compression standard framework. The algorithm is meant for embedded and non-embedded coding of wavelet coefficients inside a subband, and is called subband-block hierarchical partitioning (SBHP). It was extensively tested following the standard experiment procedures, and it was shown to yield a significant reduction in the complexity of entropy coding, with small loss in compression performance. Furthermore, it is able to seamlessly support all JPEG2000 features. We present a description of the algorithm, an analysis of its complexity, and a summary of the results obtained after its integration into the verification model (VM).

Stripe-based SPHIT lossy compression of volumetric medical images for low memory usage and uniform reconstruction quality

Abstract: This paper presents a low memory implementation of efficient lossy volumetric medical image compression using the set partitioning in hierarchical trees (SPIHT) algorithm. The coding units in this three-dimensional wavelet transform and compression method are short sequences of horizontal stripes cut from the sequence of slices in the volumetric image. As the compression degree increases, the boundaries between adjacent coding units became increasingly visible. Unlike video, where image frames are viewed under dynamic conditions, the volume image is examined under static conditions and must not exhibit such boundary artifacts. In order to eliminate them, we utilize overlapping and averaging both at the intra-slice and inter-slice boundaries between adjacent and successive stripe sequences. Our low memory implementation smooths out considerably the variation in mean squared error among different slices and suffers only an insignificant loss in performance from that of a full memory implementation.

Error-resilient compression and transmission of scalable video

Abstract: Compressed video bitstreams require protection from channel errors in a wireless channel and protection from packet loss in a wired ATM channel. The three-dimensional (3-D) SPIHT coder has proved its efficiency and its real-time capability in compression of video. A forward-error-correcting (FEC) channel (RCPC) code combined with a single ARQ (automatic- repeat-request) proved to be an effective means for protecting the bitstream. There were two problems with this scheme: the noiseless reverse channel ARQ may not be feasible in practice; and, in the absence of channel coding and ARQ, the decoded sequence was hopelessly corrupted even for relatively clean channels. In this paper, we first show how to make the 3-D SPIHT bitstream more robust to channel errors by breaking the wavelet transform into a number of spatio-temporal tree blocks which can be encoded and decoded independently. This procedure brings the added benefit of parallelization of the compression and decompression algorithms. Then we demonstrate the packetization of the bit stream and the reorganization of these packets to achieve scalability in bit rate and/or resolution in addition to robustness. Then we encode each packet with a channel code. Not only does this protect the integrity of the packets in most cases, but it also allows detection of packet decoding failures, so that only the cleanly recovered packets are reconstructed. This procedure obviates ARQ, because the performance is only about 1 dB worse than normal 3-D SPIHT with FEC and ARQ. Furthermore, the parallelization makes possible real-time implementation in hardware and software.

Stripe-based SPIHT lossy compression of volumetric medical images for low memory usage and uniform reconstruction quality

Abstract: This paper presents a low memory implementation of efficient lossy volumetric medical image compression using the set partitioning in hierarchical trees (SPIHT) algorithm. The coding units in this three-dimensional wavelet transform and compression method are short sequences of horizontal stripes cut from the sequence of slices in the volumetric image. As the compression degree increases, the boundaries between adjacent coding units became increasingly visible. Unlike video, where image frames are viewed under dynamic conditions, the volume image is examined under static conditions and must not exhibit such boundary artifacts. In order to eliminate them, we utilize overlapping both at the intra-slice and inter-slice boundaries between adjacent and successive stripe sequences. Our low memory implementation smooths out considerably the variation in mean squared error among different slices and suffers only an insignificant loss in performance from that of a full memory implementation.

Combined spatial and subband block coding of images

Abstract: This paper describes a low-memory cache efficient hybrid block coder (HBC) for images in which an image subband decomposition is partitioned into a combination of spatial blocks and subband blocks, which are independently coded. Spatial blocks contain hierarchical trees spanning subband levels, and are each encoded using the SPIHT algorithm. Subband blocks contain a block of coefficients from within a single subband, and are each encoded by the SPECK algorithm. The decomposition may have the dyadic or a wavelet packet structure. Rate is allocated amongst the sub-bitstreams produced for each block and they are packetized. The partitioning structure supports resolution embedding. The final bitstream may be progressive in fidelity or in resolution.

A new flexible bi-orthogonal filter design for multiresolution filterbanks with application to image compression

Abstract: We propose a new fast method with great potential for multiresolution pyramid decomposition of signals and images. The method allows unusual flexibility in choosing a filter for any task involving the multiresolution analysis and synthesis. Using our method, one can choose any low-pass filter for the multiresolution filtering. This method enabled us to choose the best filters for set partitioning in hierarchical trees (SPIHT) image compression for the corresponding support sizes. The compression results for our seven tap filters are better than those of the 9/7 wavelet filters and approximately the same as those of the 10/18 filters, while at the same time our seven tap filters are faster than 10/18 filters.

Semi-automatic semantic object extraction for video coding

Abstract: A semi-automatic algorithm to extract the semantic video object from image sequences is proposed. Different schemes are used to get the initial video object in the first frame and other frames of a sequence. In the first frame, two polygons are input by the user to specify the area in which the object boundary is located. Then the video object is extracted automatically based on only the first frame. In the following frames, the image frame is segmented into intensity homogeneous regions. The moving regions are detected by a morphological filter, non-moving regions are selected by the object model obtained from the previous frame. These regions form the initial video object. In each frame, after the initial object is available, the edges which belong to the video object of interest are selected by a local object contour model. Finally, an active contour model (snake) is applied to extract the final object contour.