In recent years, layered image compression is demonstrated to be a promising direction, which encodes a compact representation of the input image and apply an up-sampling network to reconstruct the image. To further improve the quality of the reconstructed image, some works transmit the semantic segment together with the compressed image data. Consequently, the compression ratio is also decreased because extra bits are required for transmitting the semantic segment. To solve this problem, we propose a new layered image compression framework with encoder-decoder matched semantic segmentation (EDMS). And then, followed by the semantic segmentation, a special convolution neural network is used to enhance the inaccurate semantic segment. As a result, the accurate semantic segment can be obtained in the decoder without requiring extra bits. The experimental results show that the proposed EDMS framework can get up to 35.31% BD-rate reduction over the HEVC-based (BPG) codec, 5% bitrate, and 24% encoding time saving compare to the state-of-the-art semantic-based image codec.
翻译:近年来,层层图像压缩被证明是一个很有希望的方向, 它将输入图像的缩放表达方式编码成一个缩略图, 并应用一个上层抽样网络来重建图像。 为了进一步提高重建图像的质量, 有些作品将语义部分与压缩图像数据一起传输。 因此, 压缩比例也有所下降, 因为传输语义部分需要额外的比特。 为了解决这个问题, 我们提议了一个新的层层图像压缩框架, 配有编码- 解码相匹配的语义分解( EDMS ) 。 然后, 在语义分解之后, 使用特殊的共振神经网络来增强不准确的语义部分。 结果, 可以在解译器中获取准确的语义部分, 而不需要额外比特 。 实验结果显示, 拟议的 EDMS 框架可以在基于 EHVC 的代碼( BPG) 代码、 5% 比特 和 24% 的编码时间保存率与基于 状态的语义图像代码比较, 。