20,829 research outputs found
Decoding billions of integers per second through vectorization
In many important applications -- such as search engines and relational
database systems -- data is stored in the form of arrays of integers. Encoding
and, most importantly, decoding of these arrays consumes considerable CPU time.
Therefore, substantial effort has been made to reduce costs associated with
compression and decompression. In particular, researchers have exploited the
superscalar nature of modern processors and SIMD instructions. Nevertheless, we
introduce a novel vectorized scheme called SIMD-BP128 that improves over
previously proposed vectorized approaches. It is nearly twice as fast as the
previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the
same time, SIMD-BP128 saves up to 2 bits per integer. For even better
compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has
a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while
being two times faster during decoding.Comment: For software, see https://github.com/lemire/FastPFor, For data, see
http://boytsov.info/datasets/clueweb09gap
RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding
We present a new hierarchical compression scheme for encoding light field
images (LFI) that is suitable for interactive rendering. Our method (RLFC)
exploits redundancies in the light field images by constructing a tree
structure. The top level (root) of the tree captures the common high-level
details across the LFI, and other levels (children) of the tree capture
specific low-level details of the LFI. Our decompressing algorithm corresponds
to tree traversal operations and gathers the values stored at different levels
of the tree. Furthermore, we use bounded integer sequence encoding which
provides random access and fast hardware decoding for compressing the blocks of
children of the tree. We have evaluated our method for 4D two-plane
parameterized light fields. The compression rates vary from 0.08 - 2.5 bits per
pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR
quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI
are 1 - 3 microseconds per channel on an NVIDIA GTX-960 and we can render new
views with a resolution of 512X512 at 200 fps. Our overall scheme is simple to
implement and involves only bit manipulations and integer arithmetic
operations.Comment: Accepted for publication at Symposium on Interactive 3D Graphics and
Games (I3D '19
Explicit measurements with almost optimal thresholds for compressed sensing
We consider the deterministic construction of a measurement
matrix and a recovery method for signals that are block
sparse. A signal that has dimension N = nd, which consists
of n blocks of size d, is called (s, d)-block sparse if
only s blocks out of n are nonzero. We construct an explicit
linear mapping Φ that maps the (s, d)-block sparse signal
to a measurement vector of dimension M, where s•d <N(1-(1-M/N)^(d/(d+1))-o(1).
We show that if the (s, d)-
block sparse signal is chosen uniformly at random then the
signal can almost surely be reconstructed from the measurement
vector in O(N^3) computations
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose
correlation is driven by the motion of objects or positioning of the vision
sensors. It concentrates on the problem where images are encoded with
compressed linear measurements. We propose a geometry-based correlation model
in order to describe the common information in pairs of images. We assume that
the constitutive components of natural images can be captured by visual
features that undergo local transformations (e.g., translation) in different
images. We first identify prominent visual features by computing a sparse
approximation of a reference image with a dictionary of geometric basis
functions. We then pose a regularized optimization problem to estimate the
corresponding features in correlated images given by quantized linear
measurements. The estimated features have to comply with the compressed
information and to represent consistent transformation between images. The
correlation model is given by the relative geometric transformations between
corresponding features. We then propose an efficient joint decoding algorithm
that estimates the compressed images such that they stay consistent with both
the quantized measurements and the correlation model. Experimental results show
that the proposed algorithm effectively estimates the correlation between
images in multi-view datasets. In addition, the proposed algorithm provides
effective decoding performance that compares advantageously to independent
coding solutions as well as state-of-the-art distributed coding schemes based
on disparity learning
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