6,002 research outputs found
Locally adaptive vector quantization: Data compression with feature preservation
A study of a locally adaptive vector quantization (LAVQ) algorithm for data compression is presented. This algorithm provides high-speed one-pass compression and is fully adaptable to any data source and does not require a priori knowledge of the source statistics. Therefore, LAVQ is a universal data compression algorithm. The basic algorithm and several modifications to improve performance are discussed. These modifications are nonlinear quantization, coarse quantization of the codebook, and lossless compression of the output. Performance of LAVQ on various images using irreversible (lossy) coding is comparable to that of the Linde-Buzo-Gray algorithm, but LAVQ has a much higher speed; thus this algorithm has potential for real-time video compression. Unlike most other image compression algorithms, LAVQ preserves fine detail in images. LAVQ's performance as a lossless data compression algorithm is comparable to that of Lempel-Ziv-based algorithms, but LAVQ uses far less memory during the coding process
Efficient Differential Pixel Value Coding in CABAC for H.264/AVC Lossless Video Compression
Abstract Since context-based adaptive binary arithmetic coding (CABAC) as the entropy coding method in H.264/AVC was originally designed for lossy video compression, it is inappropriate for lossless video compression. Based on the fact that there are statistical differences of residual data between lossy and lossless video compression, we propose an efficient differential pixel value coding method in CABAC for H.264/AVC lossless video compression. Considering the observed statistical properties of the differential pixel value in lossless coding, we modified the CABAC encoding mechanism with the newly designed binarization table and the context-modeling method. Experimental results show that the proposed method achieves an approximately 12% bit saving, compared to the original CABAC method in the H.264/AVC standard
Adaptive temporal decimation algorithm with dynamic time window
Published versio
Lossless Intra Coding in HEVC with 3-tap Filters
This paper presents a pixel-by-pixel spatial prediction method for lossless
intra coding within High Efficiency Video Coding (HEVC). A well-known previous
pixel-by-pixel spatial prediction method uses only two neighboring pixels for
prediction, based on the angular projection idea borrowed from block-based
intra prediction in lossy coding. This paper explores a method which uses three
neighboring pixels for prediction according to a two-dimensional correlation
model, and the used neighbor pixels and prediction weights change depending on
intra mode. To find the best prediction weights for each intra mode, a
two-stage offline optimization algorithm is used and a number of implementation
aspects are discussed to simplify the proposed prediction method. The proposed
method is implemented in the HEVC reference software and experimental results
show that the explored 3-tap filtering method can achieve an average 11.34%
bitrate reduction over the default lossless intra coding in HEVC. The proposed
method also decreases average decoding time by 12.7% while it increases average
encoding time by 9.7%Comment: 10 pages, 7 figure
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
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