This paper presents a discrete cosine transform (DCT)-based locally adaptive perceptual image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate. The new coder uses a locally adaptive perceptual quantization scheme based on a tractable perceptual distortion metric. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally adaptive fashion. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels in order to meet the desired target perceptual distortion. The proposed coding scheme is flexible in that it can be easily extended to work with any subband-based decomposition in addition to block-based transform methods. Compared to existing perceptual coding methods, the proposed perceptual coding method exhibits superior performance in terms of bit rate and distortion control. Coding results are presented to illustrate the performance of the presented coding scheme.

Most existing efforts in image and video compression have focused on developing methods to minimize not perceptual but rather mathematically tractable, easy to measure, distortion metrics. While nonperceptual distortion measures were found to be reasonably reliable for higher bit rates (high-quality applications), they do not correlate well with the perceived quality at lower bit rates and they fail to guarantee preservation of important perceptual qualities in the reconstructed images despite the potential for a good signal-to-noise ratio (SNR). This paper presents a perceptual-based image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate. The new coder is based on a locally adaptive perceptual quantization scheme for compressing the visual data. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally adaptive fashion based on a subband decomposition. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels to the local amount of masking present at the level of each subband transform coefficient. Compared to the existing non-locally adaptive perceptual quantization methods, the new locally adaptive algorithm exhibits superior performance and does not require additional side information. This is accomplished by estimating the amount of available masking from the already quantized data and linear prediction of the coefficient under consideration. By virtue of the local adaptation, the proposed quantization scheme is able to remove a large amount of perceptually redundant information. Since the algorithm does not require additional side information, it yields a low entropy representation of the image and is well suited for perceptually lossless image compression.

This paper presents a perceptual-based image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate. The new coder uses a locally-adaptive perceptual quantization scheme based on a tractable perceptual distortion metric. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally-adaptive fashion. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels in order to meet a desired target perceptual distortion. The proposed coding scheme is flexible in that it works with any subband-based decomposition and with block-based transform methods. Compared to the existing perceptual transform-based and block-based methods, the proposed perceptual coding method exhibits superior performance in terms of the bit rate and distortion control. Coding results are presented to illustrate the performance of the presented coding scheme.

The perceptual subband image coder (PIC) introduced by Safranek and Johnston (1989), selects a noise target level for each subband based on an empirically derived perceptual masking measure. These noise target levels are used to set the quantization level in the DPCM quantizer for every particular subband. It achieves high quality output at bit rates from 0.1 to 0.9 bits/pixel (bpp) depending on the complexity of the image. In this paper, we present an algorithm that locally adapts the quantizer step size at each pixel according to an estimate of the masking measure. This estimate is based on the already coded pixels and predictions of the not yet coded pixels. Compared to the PIC, the proposed method does not require any additional side information. In fact, it eliminates the need to transmit the quantizer step size for each subband. For comparable perceptual quality, the proposed method achieves compression gains up to 40 percent. Typical values are in the order of 20 to 30 percent, depending on the nature of the image. Our algorithm has also better performance for supra-threshold image compression since the perceptual error is distributed more evenly and is not concentrated in the most sensitive regions.

Embedded zerotree wavelet coding (EZW), introduced by Shapiro (1993) and improved by Said and Pearlman (1996) using an algorithm based on set partitioning in hierarchical trees (SPIHT), is a computationally inexpensive image coding technique and has proven to be very effective at minimizing the mean-square error (MSE) distortion measure. However, minimizing MSE does not guarantee preservation of good perceptual qualities in the decoded image, especially at low bit rates. In this paper we propose a perceptually-tuned embedded zerotree image codec (PEZ) that introduces a perceptual weighting to the wavelet transform coefficients prior to EZW encoding. In this coder the EZW minimizes a perceptually based distortion measure instead of MSE. The perceptual weights for all subbands are computed based on the just noticeable distortion (JND) thresholds for uniform noise. The new perceptually tuned codec has the same complexity as the original EZW/SPIHT techniques and results in a comparable or superior coding performance. Coding results are presented to illustrate the performance of the new coder.