3,644 research outputs found
Visual Attention for Region of Interest Coding in JPEG 2000
This paper details work undertaken on the application of an algorithm for visual attention (VA) to region of interest (ROI) coding in JPEG 2000 (JP2K). In this way, an "interest ordered" progressive bit-stream is produced where the regions highlighted by the VA algorithm are presented first in bit-stream. The paper briefly outlines the terminology used in JP2K, the packet structure of the bit-stream, and the methods available to achieve ROI coding in JP2K (tiling, coefficient scaling, and code-block selection). The paper then describes how the output of the VA algorithm is post-processed so that an ROI is produced that can be efficiently coded using coefficient scaling in JP2K. Finally, a two alternative forced choice (2AFC) visual trial is undertaken to compare the visual quality of images encoded using the proposed VA ROI algorithm and conventional JP2K. The experimental results show that, while there is no overall preference for the VA ROI encoded images; there is an improvement in perceived image quality at low bit rates (below 0.25 bits per pixel). It is concluded that an overall increase in image quality only occurs when the increase in quality of the ROI more than compensates for the decrease in quality of the image background (i.e., non-ROI)
Semantic Perceptual Image Compression using Deep Convolution Networks
It has long been considered a significant problem to improve the visual
quality of lossy image and video compression. Recent advances in computing
power together with the availability of large training data sets has increased
interest in the application of deep learning cnns to address image recognition
and image processing tasks. Here, we present a powerful cnn tailored to the
specific task of semantic image understanding to achieve higher visual quality
in lossy compression. A modest increase in complexity is incorporated to the
encoder which allows a standard, off-the-shelf jpeg decoder to be used. While
jpeg encoding may be optimized for generic images, the process is ultimately
unaware of the specific content of the image to be compressed. Our technique
makes jpeg content-aware by designing and training a model to identify multiple
semantic regions in a given image. Unlike object detection techniques, our
model does not require labeling of object positions and is able to identify
objects in a single pass. We present a new cnn architecture directed
specifically to image compression, which generates a map that highlights
semantically-salient regions so that they can be encoded at higher quality as
compared to background regions. By adding a complete set of features for every
class, and then taking a threshold over the sum of all feature activations, we
generate a map that highlights semantically-salient regions so that they can be
encoded at a better quality compared to background regions. Experiments are
presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset,
in which our algorithm achieves higher visual quality for the same compressed
size.Comment: Accepted to Data Compression Conference, 11 pages, 5 figure
Fast object detection in compressed JPEG Images
Object detection in still images has drawn a lot of attention over past few
years, and with the advent of Deep Learning impressive performances have been
achieved with numerous industrial applications. Most of these deep learning
models rely on RGB images to localize and identify objects in the image.
However in some application scenarii, images are compressed either for storage
savings or fast transmission. Therefore a time consuming image decompression
step is compulsory in order to apply the aforementioned deep models. To
alleviate this drawback, we propose a fast deep architecture for object
detection in JPEG images, one of the most widespread compression format. We
train a neural network to detect objects based on the blockwise DCT (discrete
cosine transform) coefficients {issued from} the JPEG compression algorithm. We
modify the well-known Single Shot multibox Detector (SSD) by replacing its
first layers with one convolutional layer dedicated to process the DCT inputs.
Experimental evaluations on PASCAL VOC and industrial dataset comprising images
of road traffic surveillance show that the model is about faster than
regular SSD with promising detection performances. To the best of our
knowledge, this paper is the first to address detection in compressed JPEG
images
Can Region of Interest Coding Improve Overall Perceived Image Quality?
In this paper we review a number of approaches to reducing, or removing, the problem of shift variance in the discrete wavelet transform (DWT). We describe a generalization of the critically sampled DWT and the fully sampled 'algorithme a trous' that provides approximate shift-invariance with an acceptable level of redundancy. The proposed over complete DWT (OCDWT) is critically sub-sampled to a given level of the decomposition, below which it is then fully sampled. The efficacy of the proposed algorithm is illustrated in an edge detection context and directly compared to a number of other shift-invariant transforms in terms of complexity and redundancy
A reduced-reference perceptual image and video quality metric based on edge preservation
In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric. © 2012 Martini et al
A survey of parallel algorithms for fractal image compression
This paper presents a short survey of the key research work that has been undertaken in the application of parallel algorithms for Fractal image compression. The interest in fractal image compression techniques stems from their ability to achieve high compression ratios whilst maintaining a very high quality in the reconstructed image. The main drawback of this compression method is the very high computational cost that is associated with the encoding phase. Consequently, there has been significant interest in exploiting parallel computing architectures in order to speed up this phase, whilst still maintaining the advantageous features of the approach. This paper presents a brief introduction to fractal image compression, including the iterated function system theory upon
which it is based, and then reviews the different techniques that have been, and can be, applied in order to parallelize the compression algorithm
Space-variant picture coding
PhDSpace-variant picture coding techniques exploit the strong spatial non-uniformity of
the human visual system in order to increase coding efficiency in terms of perceived quality
per bit. This thesis extends space-variant coding research in two directions. The first of
these directions is in foveated coding. Past foveated coding research has been dominated
by the single-viewer, gaze-contingent scenario. However, for research into the multi-viewer
and probability-based scenarios, this thesis presents a missing piece: an algorithm for computing
an additive multi-viewer sensitivity function based on an established eye resolution
model, and, from this, a blur map that is optimal in the sense of discarding frequencies in
least-noticeable- rst order. Furthermore, for the application of a blur map, a novel algorithm
is presented for the efficient computation of high-accuracy smoothly space-variant
Gaussian blurring, using a specialised filter bank which approximates perfect space-variant
Gaussian blurring to arbitrarily high accuracy and at greatly reduced cost compared to
the brute force approach of employing a separate low-pass filter at each image location.
The second direction is that of artifi cially increasing the depth-of- field of an image, an
idea borrowed from photography with the advantage of allowing an image to be reduced
in bitrate while retaining or increasing overall aesthetic quality. Two synthetic depth of field algorithms are presented herein, with the desirable properties of aiming to mimic
occlusion eff ects as occur in natural blurring, and of handling any number of blurring
and occlusion levels with the same level of computational complexity. The merits of this
coding approach have been investigated by subjective experiments to compare it with
single-viewer foveated image coding. The results found the depth-based preblurring to
generally be significantly preferable to the same level of foveation blurring
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