121 research outputs found
Convex Optimization Based Bit Allocation for Light Field Compression under Weighting and Consistency Constraints
Compared with conventional image and video, light field images introduce the
weight channel, as well as the visual consistency of rendered view, information
that has to be taken into account when compressing the pseudo-temporal-sequence
(PTS) created from light field images. In this paper, we propose a novel frame
level bit allocation framework for PTS coding. A joint model that measures
weighted distortion and visual consistency, combined with an iterative encoding
system, yields the optimal bit allocation for each frame by solving a convex
optimization problem. Experimental results show that the proposed framework is
effective in producing desired distortion distribution based on weights, and
achieves up to 24.7% BD-rate reduction comparing to the default rate control
algorithm.Comment: published in IEEE Data Compression Conference, 201
Quantum multipartite maskers vs quantum error-correcting codes
Since masking of quantum information was introduced by Modi et al. in [PRL
120, 230501 (2018)], many discussions on this topic have been published. In
this paper, we consider relationship between quantum multipartite maskers
(QMMs) and quantum error-correcting codes (QECCs). We say that a subset of
pure states of a system can be masked by an operator into a
multipartite system \H^{(n)} if all of the image states of states
in have the same marginal states on each subsystem. We call such
an a QMM of . By establishing an expression of a QMM, we obtain a
relationship between QMMs and QECCs, which reads that an isometry is a QMM of
all pure states of a system if and only if its range is a QECC of any
one-erasure channel. As an application, we prove that there is no an isometric
universal masker from \C^2 into \C^2\otimes\C^2\otimes\C^2 and then the
states of \C^3 can not be masked isometrically into
\C^2\otimes\C^2\otimes\C^2. This gives a consummation to a main result and
leads to a negative answer to an open question in [PRA 98, 062306 (2018)].
Another application is that arbitrary quantum states of \C^d can be
completely hidden in correlations between any two subsystems of the tripartite
system \C^{d+1}\otimes\C^{d+1}\otimes\C^{d+1}, while arbitrary quantum states
cannot be completely hidden in the correlations between subsystems of a
bipartite system [PRL 98, 080502 (2007)].Comment: This is a revision about arXiv:2004.14540. In the present version,
and old Eq. (2.2) have been exchanged and the followed three
equations have been correcte
A Bayesian Approach to Block Structure Inference in AV1-based Multi-rate Video Encoding
Due to differences in frame structure, existing multi-rate video encoding
algorithms cannot be directly adapted to encoders utilizing special reference
frames such as AV1 without introducing substantial rate-distortion loss. To
tackle this problem, we propose a novel bayesian block structure inference
model inspired by a modification to an HEVC-based algorithm. It estimates the
posterior probabilistic distributions of block partitioning, and adapts early
terminations in the RDO procedure accordingly. Experimental results show that
the proposed method provides flexibility for controlling the tradeoff between
speed and coding efficiency, and can achieve an average time saving of 36.1%
(up to 50.6%) with negligible bitrate cost.Comment: published in IEEE Data Compression Conference, 201
Simultaneous Production of Sugar and Ethanol from Sugarcane in China, the Development, Research and Prospect Aspects
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
We study the problem of extracting accurate correspondences for point cloud
registration. Recent keypoint-free methods have shown great potential through
bypassing the detection of repeatable keypoints which is difficult to do
especially in low-overlap scenarios. They seek correspondences over downsampled
superpoints, which are then propagated to dense points. Superpoints are matched
based on whether their neighboring patches overlap. Such sparse and loose
matching requires contextual features capturing the geometric structure of the
point clouds. We propose Geometric Transformer, or GeoTransformer for short, to
learn geometric feature for robust superpoint matching. It encodes pair-wise
distances and triplet-wise angles, making it invariant to rigid transformation
and robust in low-overlap cases. The simplistic design attains surprisingly
high matching accuracy such that no RANSAC is required in the estimation of
alignment transformation, leading to times acceleration. Extensive
experiments on rich benchmarks encompassing indoor, outdoor, synthetic,
multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our
method improves the inlier ratio by percentage points and the
registration recall by over points on the challenging 3DLoMatch benchmark.
Our code and models are available at
\url{https://github.com/qinzheng93/GeoTransformer}.Comment: Accepted by TPAMI. Extended version of our CVPR 2022 paper
[arXiv:2202.06688
SALI: A Scalable Adaptive Learned Index Framework based on Probability Models
The growth in data storage capacity and the increasing demands for high
performance have created several challenges for concurrent indexing structures.
One promising solution is learned indexes, which use a learning-based approach
to fit the distribution of stored data and predictively locate target keys,
significantly improving lookup performance. Despite their advantages,
prevailing learned indexes exhibit constraints and encounter issues of
scalability on multi-core data storage.
This paper introduces SALI, the Scalable Adaptive Learned Index framework,
which incorporates two strategies aimed at achieving high scalability,
improving efficiency, and enhancing the robustness of the learned index.
Firstly, a set of node-evolving strategies is defined to enable the learned
index to adapt to various workload skews and enhance its concurrency
performance in such scenarios. Secondly, a lightweight strategy is proposed to
maintain statistical information within the learned index, with the goal of
further improving the scalability of the index. Furthermore, to validate their
effectiveness, SALI applied the two strategies mentioned above to the learned
index structure that utilizes fine-grained write locks, known as LIPP. The
experimental results have demonstrated that SALI significantly enhances the
insertion throughput with 64 threads by an average of 2.04x compared to the
second-best learned index. Furthermore, SALI accomplishes a lookup throughput
similar to that of LIPP+.Comment: Accepted by Conference SIGMOD 24, June 09-15, 2024, Santiago, Chil
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