Color-Based Coding Unit Level Adaptive Quantization for HEVC
September 15, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Lee Prangnell, Victor Sanchez
arXiv ID
1609.06302
Category
cs.MM: Multimedia
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
HEVC HM 16 includes a Coding Unit (CU) level perceptual quantization technique named AdaptiveQP. AdaptiveQP adjusts the Quantization Parameter (QP) at the CU level based on the spatial activity of samples in the four constituent NxN sub-blocks of the luma Coding Block (CB), which is contained within a 2Nx2N CU. In this paper, we propose C-BAQ, which, in contrast to AdaptiveQP, adjusts the CU level QP according to the spatial activity of samples in the four constituent NxN sub-blocks of both the luma and chroma CBs. By computing the sum of luma, chroma Cb and chroma Cr spatial activity in a CU, a richer reflection of spatial activity in the CU is attained. Therefore, a more appropriate CU level QP can be selected, thus leading to important improvements in terms of coding efficiency. We evaluate the proposed technique in HEVC HM 16.7 using 4:4:4, 4:2:2 and 4:2:0 YCbCr sequences. Both subjective and objective evaluations are undertaken during which we compare C-BAQ with AdaptiveQP. The objective evaluation reveals that C-BAQ attains a maximum BD-Rate reduction of 15.9% (Y), 13.1% (Cr) and 16.1% (Cb) in addition to a maximum decoding time reduction of 11.0%.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multimedia
π
π
Old Age
R.I.P.
π»
Ghosted
Viewport-Adaptive Navigable 360-Degree Video Delivery
π
π
The Cartographer
A Comprehensive Survey on Cross-modal Retrieval
π
π
The Cartographer
An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
R.I.P.
π»
Ghosted
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
R.I.P.
π»
Ghosted
Video Generation From Text
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted