179 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
Bit Allocation using Optimization
In this paper, we consider the problem of bit allocation in neural video
compression (NVC). Due to the frame reference structure, current NVC methods
using the same R-D (Rate-Distortion) trade-off parameter for all
frames are suboptimal, which brings the need for bit allocation. Unlike
previous methods based on heuristic and empirical R-D models, we propose to
solve this problem by gradient-based optimization. Specifically, we first
propose a continuous bit implementation method based on Semi-Amortized
Variational Inference (SAVI). Then, we propose a pixel-level implicit bit
allocation method using iterative optimization by changing the SAVI target.
Moreover, we derive the precise R-D model based on the differentiable trait of
NVC. And we show the optimality of our method by proofing its equivalence to
the bit allocation with precise R-D model. Experimental results show that our
approach significantly improves NVC methods and outperforms existing bit
allocation methods. Our approach is plug-and-play for all differentiable NVC
methods, and it can be directly adopted on existing pre-trained models
Correcting the Sub-optimal Bit Allocation
In this paper, we investigate the problem of bit allocation in Neural Video
Compression (NVC). First, we reveal that a recent bit allocation approach
claimed to be optimal is, in fact, sub-optimal due to its implementation.
Specifically, we find that its sub-optimality lies in the improper application
of semi-amortized variational inference (SAVI) on latent with non-factorized
variational posterior. Then, we show that the corrected version of SAVI on
non-factorized latent requires recursively applying back-propagating through
gradient ascent, based on which we derive the corrected optimal bit allocation
algorithm. Due to the computational in-feasibility of the corrected bit
allocation, we design an efficient approximation to make it practical.
Empirical results show that our proposed correction significantly improves the
incorrect bit allocation in terms of R-D performance and bitrate error, and
outperforms all other bit allocation methods by a large margin. The source code
is provided in the supplementary material
Multiple description video coding for real-time applications using HEVC
Remote control vehicles require the transmission of large amounts of data,
and video is one of the most important sources for the driver. To ensure
reliable video transmission, the encoded video stream is transmitted
simultaneously over multiple channels. However, this solution incurs a high
transmission cost due to the wireless channel's unreliable and random bit loss
characteristics. To address this issue, it is necessary to use more efficient
video encoding methods that can make the video stream robust to noise. In this
paper, we propose a low-complexity, low-latency 2-channel Multiple Description
Coding (MDC) solution with an adaptive Instantaneous Decoder Refresh (IDR)
frame period, which is compatible with the HEVC standard. This method shows
better resistance to high packet loss rates with lower complexity
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