7,816 research outputs found
Learning Convolutional Networks for Content-weighted Image Compression
Lossy image compression is generally formulated as a joint rate-distortion
optimization to learn encoder, quantizer, and decoder. However, the quantizer
is non-differentiable, and discrete entropy estimation usually is required for
rate control. These make it very challenging to develop a convolutional network
(CNN)-based image compression system. In this paper, motivated by that the
local information content is spatially variant in an image, we suggest that the
bit rate of the different parts of the image should be adapted to local
content. And the content aware bit rate is allocated under the guidance of a
content-weighted importance map. Thus, the sum of the importance map can serve
as a continuous alternative of discrete entropy estimation to control
compression rate. And binarizer is adopted to quantize the output of encoder
due to the binarization scheme is also directly defined by the importance map.
Furthermore, a proxy function is introduced for binary operation in backward
propagation to make it differentiable. Therefore, the encoder, decoder,
binarizer and importance map can be jointly optimized in an end-to-end manner
by using a subset of the ImageNet database. In low bit rate image compression,
experiments show that our system significantly outperforms JPEG and JPEG 2000
by structural similarity (SSIM) index, and can produce the much better visual
result with sharp edges, rich textures, and fewer artifacts
Motion estimation and CABAC VLSI co-processors for real-time high-quality H.264/AVC video coding
Real-time and high-quality video coding is gaining a wide interest in the research and industrial community for different applications. H.264/AVC, a recent standard for high performance video coding, can be successfully exploited in several scenarios including digital video broadcasting, high-definition TV and DVD-based systems, which require to sustain up to tens of Mbits/s. To that purpose this paper proposes optimized architectures for H.264/AVC most critical tasks, Motion estimation and context adaptive binary arithmetic coding. Post synthesis results on sub-micron CMOS standard-cells technologies show that the proposed architectures can actually process in real-time 720 Ă— 480 video sequences at 30 frames/s and grant more than 50 Mbits/s. The achieved circuit complexity and power consumption budgets are suitable for their integration in complex VLSI multimedia systems based either on AHB bus centric on-chip communication system or on novel Network-on-Chip (NoC) infrastructures for MPSoC (Multi-Processor System on Chip
A VLSI architecture of JPEG2000 encoder
Copyright @ 2004 IEEEThis paper proposes a VLSI architecture of JPEG2000 encoder, which functionally consists of two parts: discrete wavelet transform (DWT) and embedded block coding with optimized truncation (EBCOT). For DWT, a spatial combinative lifting algorithm (SCLA)-based scheme with both 5/3 reversible and 9/7 irreversible filters is adopted to reduce 50% and 42% multiplication computations, respectively, compared with the conventional lifting-based implementation (LBI). For EBCOT, a dynamic memory control (DMC) strategy of Tier-1 encoding is adopted to reduce 60% scale of the on-chip wavelet coefficient storage and a subband parallel-processing method is employed to speed up the EBCOT context formation (CF) process; an architecture of Tier-2 encoding is presented to reduce the scale of on-chip bitstream buffering from full-tile size down to three-code-block size and considerably eliminate the iterations of the rate-distortion (RD) truncation.This work was supported in part by the China National High Technologies Research Program (863) under Grant 2002AA1Z142
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