Learned Image Compression with Dual-Branch Encoder and Conditional Information Coding
arxiv(2024)
摘要
Recent advancements in deep learning-based image compression are notable.
However, prevalent schemes that employ a serial context-adaptive entropy model
to enhance rate-distortion (R-D) performance are markedly slow. Furthermore,
the complexities of the encoding and decoding networks are substantially high,
rendering them unsuitable for some practical applications. In this paper, we
propose two techniques to balance the trade-off between complexity and
performance. First, we introduce two branching coding networks to independently
learn a low-resolution latent representation and a high-resolution latent
representation of the input image, discriminatively representing the global and
local information therein. Second, we utilize the high-resolution latent
representation as conditional information for the low-resolution latent
representation, furnishing it with global information, thus aiding in the
reduction of redundancy between low-resolution information. We do not utilize
any serial entropy models. Instead, we employ a parallel channel-wise
auto-regressive entropy model for encoding and decoding low-resolution and
high-resolution latent representations. Experiments demonstrate that our method
is approximately twice as fast in both encoding and decoding compared to the
parallelizable checkerboard context model, and it also achieves a 1.2
improvement in R-D performance compared to state-of-the-art learned image
compression schemes. Our method also outperforms classical image codecs
including H.266/VVC-intra (4:4:4) and some recent learned methods in
rate-distortion performance, as validated by both PSNR and MS-SSIM metrics on
the Kodak dataset.
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