358 research outputs found
Quality and Rate Control of JPEG XR
Driven by the need for seismic data compression with high dynamic range and 32-bit resolution, we propose two algorithms to efficiently and precisely control the signal-to-noise ratio (SNR) and bit rate in JPEG XR image compression to allow users to compress seismic data with a target SNR or a target bit rate. Based on the quantization properties of JPEG XR and the nature of blank macroblocks, we build a reliable model between the quantization parameter (QP) and SNR. This enables us to estimate the right QP with target quality for the JPEG XR encoder
A Similarity Measure for Material Appearance
We present a model to measure the similarity in appearance between different
materials, which correlates with human similarity judgments. We first create a
database of 9,000 rendered images depicting objects with varying materials,
shape and illumination. We then gather data on perceived similarity from
crowdsourced experiments; our analysis of over 114,840 answers suggests that
indeed a shared perception of appearance similarity exists. We feed this data
to a deep learning architecture with a novel loss function, which learns a
feature space for materials that correlates with such perceived appearance
similarity. Our evaluation shows that our model outperforms existing metrics.
Last, we demonstrate several applications enabled by our metric, including
appearance-based search for material suggestions, database visualization,
clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure
Development of Novel Image Compression Algorithms for Portable Multimedia Applications
Portable multimedia devices such as digital camera, mobile d
evices, personal digtal assistants (PDAs), etc. have limited memory, battery life and processing power.
Real time processing and transmission using these devices requires image compression algorithms that can compress efficiently with reduced complexity. Due to limited
resources, it is not always possible to implement the best algorithms inside these devices. In uncompressed form, both raw and image data occupy an unreasonably large
space. However, both raw and image data have a significant amount of statistical and
visual redundancy. Consequently, the used storage space can be efficiently reduced by compression. In this thesis, some novel low complexity and embedded image compression algorithms are developed especially suitable for low bit rate image compression using these devices.
Despite the rapid progress in the Internet and multimedia technology, demand for data storage and data transmission bandwidth continues to outstrip the capabil-
ities of available technology. The browsing of images over In ternet from the image data sets using these devices requires fast encoding and decodin
g speed with better rate-distortion performance. With progressive picture build up of the wavelet based
coded images, the recent multimedia applications demand goo
d quality images at the earlier stages of transmission. This is particularly important if the image is browsed
over wireless lines where limited channel capacity, storage and computation are the deciding parameters. Unfortunately, the performance of JPEG codec degrades at low bit rates because of underlying block based DCT transforms. Altho
ugh wavelet based codecs provide substantial improvements in progressive picture quality at lower bit
rates, these coders do not fully exploit the coding performance at lower bit rates. It is evident from the statistics of transformed images that the number of significant coefficients having magnitude higher than earlier thresholds are very few. These wavelet
based codecs code zero to each insignificant subband as it moves from coarsest to finest subbands. It is also demonstrated that there could be six to sev
en bit plane passes where wavelet coders encode many zeros as many subbands are likely to be insignificant with respect to early thresholds. Bits indicating
insignificance of a coefficient or subband are required, but they don’t code information that reduces distortion
of the reconstructed image. This leads to reduction of zero distortion for an increase in non zero bit-rate.
Another problem associated with wavelet based coders such as Set partitioning in hierarchical trees (SPIHT), Set partitioning embedded block (SPECK), Wavelet
block-tree coding (WBTC) is because of the use of auxiliary lists. The size of list data structures increase exponentially as more and more eleme
nts are added, removed or moved in each bitplane pass. This increases the dynamic memory requirement
of the codec, which is a less efficient feature for hardware implementations. Later,
many listless variants of SPIHT and SPECK, e.g. No list SPIHT (NLS) and Listless SPECK (LSK) respectively are developed. However, these algorithms have similar
rate distortion performances, like the list based coders. An improved LSK (ILSK)algorithm proposed in this dissertation that improves the low b
it rate performance of LSK by encoding much lesser number of symbols (i.e. zeros) to several insignificant
subbands. Further, the ILSK is combined with a block based transform known as discrete Tchebichef transform (DTT). The proposed new coder isnamed as Hierar-chical listless DTT (HLDTT). DTT is chosen over DCT because of it’s
similar energy compaction property like discrete cosine transform (DCT). It is demonstrated that
the decoded image quality using HLDTT has better visual performance (i.e., Mean Structural Similarity) than the images decoded using DCT based embedded coders
in most of the bit rates.
The ILSK algorithm is also combined with Lift based wavelet tra nsform to show the superiority over JPEG2000 at lower rates in terms of peak signal-to-noise ratio
(PSNR). A full-scalable and random access decodable listless algorithm is also developed which is based on lift based ILSK. The proposed algorithm named as scalable
listless embedded block partitioning (S-LEBP) generates bit
stream that offer increasing signal-to-noise ratio and spatial resolution. These are very useful features for
transmission of images in a heterogeneous network that optimally service each user according to available bandwidth and computing needs. Random access decoding is a
very useful feature for extracting/manipulating certain ar
ea of an image with minimal decoding work. The idea used in ILSK is also extended to encode and decode color
images. The proposed algorithm for coding color images is named as Color listless embedded block partitioning (CLEBP) algorithm. The coding efficiency of CLEBP is compared with Color SPIHT (CSPIHT) and color variant of WBTC
algorithm. From the simulation results, it is shown that CLEBP exhibits a significant PSNR performance improvement over the later two algorithms on various types of images.
Although many modifications to NLS and LSK have been made, the listless modification to WBTC algorithm has not been reported in the literature. Therefore,a listless variant of WBTC (named as LBTC) algorithm is proposed. LBTC not
only reduces the memory requirement by 88-89% but also increases the encoding and decoding speed, while preserving the rate-distortion perform ance at the same time.
Further, the combination of DCT with LBTC (named as DCT
LBT) and DTT with LBTC (named as Hierarchical listless DTT, HLBTDTT) are compared with some state-of-the-art DCT based embedded coders. It is also shown that
the proposed DCT-LBT and HLBT-DTT show significant PSNR improvements over almost all the embedded coders in most of the bit rates.
In some multimedia applications e.g., digital camera, camco
rders etc., the images always need to have a fixed pre-determined high quality. The extra effort required for
quality scalability is wasted. Therefore, non-embedded algo
rithms are best suited for these applications. The proposed algorithms can be made non-embedded by encoding
a fixed set of bit planes at a time. Instead, a sparse orthogonal transform matrix is proposed, which can be integrated in a JEPG baseline coder. The proposed matrix
promises a substantial reduction in hardware complexity with amarginal loss of image quality on a considerable range of bit rates than block based DCT or Integer DCT
VHDL modeling and synthesis of the JPEG-XR inverse transform
This work presents a pipelined VHDL implementation of the inverse lapped biorthogonal transform used in the decompression process of the soon to be released JPEG-XR still image standard format. This inverse transform involves integer only calculations using lifting operations and Kronecker products. Divisions and multiplications by small integer coefficients are implemented using a bit shift and add technique resulting in a multiplier-less implementation with 736 instances of addition. When targeted to an Altera Stratix II FPGA with a 50 MHz system clock, this design is capable of completing the inverse transform of an 8400 x 6600 pixel image in less than 70 ms
Image Transmission over Resource-constrained Low-Power Radio Networks
The transmission of large amounts of data over resource-constrained radio frequency (RF) networks is impacted by regulatory constraints and can affect reliability due to channel congestion. These barriers limit the use case to specific applications. This research extends the use case scenario to include the transmission of digital images over such networks which to date has not been widely documented. To achieve this, the overall data volume needs to be reduced to manageable limits. Drawing on previous theoretical work this research explored, developed and implemented novel image compression techniques suitable for use in resource-constrained RF networks.
A compression technique was developed which allows variable compression ratios to be selected dependent on the specific use case. This was implemented in an end-to-end low-power radio network operating in license-free spectrum using a customised radio frequency testbed. The robust compression scheme which was developed here enabled out-of-sequence packet reception, further increasing the reliability of the transmission.
To allow detailed viewing of a region of interest (ROI) within a large format image (quarter video graphics array) to be transmitted, a novel algorithm was designed and implemented. This enabled the transmission of a region of interest (ROI) in an uncompressed format as a stand-alone image portion, or in combination with a fully compressed image. Significantly, this yielded flexibility in the quantity of data to be transmitted which could increase the lifespan of battery powered devices. A further development allowed direct manipulation of individual image pixels. This permitted additional data, such as battery voltage level to be directly embedded in the transmitted image data. An advantage of this innovative method was that it did not incur any extra overhead in data volume requirements.
The embodied system developed is an agnostic image compression algorithm and is suitable for use with resource-constrained devices and networks. Results showed that high compression ratios (70%) with good peak signal-to-noise ratio (PSNR) of approximately 36dB was achievable for a complete end-to-end transmission system
Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah
Wi-Fi systems based on the IEEE 802.11 standards are the most popular
wireless interfaces that use Listen Before Talk (LBT) method for channel
access. The distinctive feature of a majority of LBT-based systems is that the
transmitters use preambles that precede the data to allow the receivers to
perform packet detection and carrier frequency offset (CFO) estimation.
Preambles usually contain repetitions of training symbols with good correlation
properties, while conventional digital receivers apply correlation-based
methods for both packet detection and CFO estimation. However, in recent years,
data-based machine learning methods are disrupting physical layer research.
Promising results have been presented, in particular, in the domain of deep
learning (DL)-based channel estimation. In this paper, we present a performance
and complexity analysis of packet detection and CFO estimation using both the
conventional and the DL-based approaches. The goal of the study is to
investigate under which conditions the performance of the DL-based methods
approach or even surpass the conventional methods, but also, under which
conditions their performance is inferior. Focusing on the emerging IEEE
802.11ah standard, our investigation uses both the standard-based simulated
environment, and a real-world testbed based on Software Defined Radios.Comment: 13 pages, journal publicatio
The Galactic Exoplanet Survey Telescope (GEST)
The Galactic Exoplanet Survey Telescope (GEST) will observe a 2 square degree
field in the Galactic bulge to search for extra-solar planets using a
gravitational lensing technique. This gravitational lensing technique is the
only method employing currently available technology that can detect Earth-mass
planets at high signal-to-noise, and can measure the frequency of terrestrial
planets as a function of Galactic position. GEST's sensitivity extends down to
the mass of Mars, and it can detect hundreds of terrestrial planets with
semi-major axes ranging from 0.7 AU to infinity. GEST will be the first truly
comprehensive survey of the Galaxy for planets like those in our own Solar
System.Comment: 17 pages with 13 figures, to be published in Proc. SPIE vol 4854,
"Future EUV-UV and Visible Space Astrophysics Missions and Instrumentation
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