70,051 research outputs found
Lossless Image Compression Using Super-Spatial Structure Prediction
Digital Object Identifier 10.1109/LSP.2010.2040925We recognize that the key challenge in image
compression is to efficiently represent and encode high-frequency image structure components, such as edges, patterns, and textures. In this work, we develop an efficient lossless image compression scheme called super-spatial structure prediction. This super-spatial prediction is motivated by motion prediction in video coding, attempting to find an optimal prediction of structure components within previously encoded image regions. We find that this super-spatial prediction is very efficient for image regions with significant structure components. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art lossless image compression methods
Efficient depth image compression using accurate depth discontinuity detection and prediction
This paper presents a novel depth image compression algorithm for both 3D Television (3DTV) and Free Viewpoint Television (FVTV) services. The proposed scheme adopts the K-means clustering algorithm to segment the depth image into K segments. The resulting segmented image is losslessly compressed and transmitted to the decoder. The depth image is then compressed using a bi-modal block encoder, where the smooth blocks are predicted using direct spatial prediction. On the other hand, blocks containing depth discontinuities are approximated using a novel depth discontinuity predictor. The residual information is then compressed using a lossy compression strategy and transmitted to the receiver. Simulation results indicate that the proposed scheme outperforms the state of the art spatial video coding systems available today such as JPEG and H.264/AVC Intra. Moreover, the proposed scheme manages to outperform specialized depth image compression algorithms such as the one proposed by Zanuttigh and Cortelazzo.peer-reviewe
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
Quality Classified Image Analysis with Application to Face Detection and Recognition
Motion blur, out of focus, insufficient spatial resolution, lossy compression
and many other factors can all cause an image to have poor quality. However,
image quality is a largely ignored issue in traditional pattern recognition
literature. In this paper, we use face detection and recognition as case
studies to show that image quality is an essential factor which will affect the
performances of traditional algorithms. We demonstrated that it is not the
image quality itself that is the most important, but rather the quality of the
images in the training set should have similar quality as those in the testing
set. To handle real-world application scenarios where images with different
kinds and severities of degradation can be presented to the system, we have
developed a quality classified image analysis framework to deal with images of
mixed qualities adaptively. We use deep neural networks first to classify
images based on their quality classes and then design a separate face detector
and recognizer for images in each quality class. We will present experimental
results to show that our quality classified framework can accurately classify
images based on the type and severity of image degradations and can
significantly boost the performances of state-of-the-art face detector and
recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page
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