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Low-complexity lossy image coding through a near-optimal general embedded quantizer

机译：通过接近最佳的通用嵌入式量化器进行低复杂度有损图像编码

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摘要

Embedded quantization is a mechanism employed by many lossy image codecs to progressively refine the distortion of a (transformed) image. Currently, the most common scheme to do so is to use a uniform scalar deadzone quantizer (USDQ) together with a bitplane coding (BPC) strategy. This scheme is convenient, but does not allow major variations. This paper uses the recently introduced general embedded quantizer (GEQ) to design a multi-stage quantization scheme that can be introduced in the core of modern image coding systems. Experimental results carried out in the framework of JPEG2000 indicate that the proposed scheme achieves same coding performance as that of USDQ+BPC while requiring fewer quantization stages, which reduces the computational costs of codecs without penalizing their performance.
机译：嵌入式量化是许多有损图像编解码器采用的一种机制，用于逐步完善（变换后的）图像的失真。当前，最常见的方案是使用统一的标量死区量化器（USDQ）和位平面编码（BPC）策略。此方案很方便，但不允许进行重大更改。本文使用最近引入的通用嵌入式量化器（GEQ）来设计一种多级量化方案，该方案可以引入现代图像编码系统的核心。在JPEG2000框架下进行的实验结果表明，该方案可实现与USDQ + BPC相同的编码性能，同时所需的量化级更少，从而降低了编解码器的计算成本，而不会降低其性能。

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