Transform coding of monochrome and color images using trellis coded quantization

Marcellin, Michael W.; Sriram, P.; Tong, Kai-Loong

doi:10.1109/76.257216

Cited by 28 publications

(7 citation statements)

References 27 publications

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“…Due to its excellent MSE performance and moderate complexity, TCQ-based schemes have been widely applied to image compression [2]- [5]. Among these, Marcellin et al combined the DCT with fixed-rate TCQ and entropy-constrained TCQ (ECTCQ) in [2]. They also combined the wavelet transform with ECTCQ in [3].…”

Section: Introductionmentioning

confidence: 99%

Uniform threshold TCQ with block classification for image transmission over noisy channels

Cai

Chen

2001

IEEE Trans. Circuits Syst. Video Technol.

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A combined source-channel coding scheme without explicit error protection is proposed to transmit images over noisy channels. Major components of the proposed coding scheme include 2-D DCT with block classification, fixed-length uniform threshold trellis coded quantization, optimal bit-allocation algorithm, and noise reduction filters. The integration of these components allows us to organize the compressed bitstream in such a way that it is less sensitive to channel noise, and hence achieves data compression and error resilience at the same time. This paper reports our recent study by incorporating the block classification into the integrated scheme. Experimental results show that, in the case of noise-free channels and at the bit rate of 0.5 bpp, an improvement of 2.33 dB can be achieved with the classification. In the case of noisy channels, the gain decreases as the bit error rate increases. However, we can still achieve an average improvement of 0.46 dB, even for highly noisy channels with BER = 0 1. Our proposed system uses no error protection, no synchronization codewords and no entropy coding. However, it shows decent compression ratio and graceful degradation with respect to increasing channel errors.

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Section: Introductionmentioning

confidence: 99%

Uniform threshold TCQ with block classification for image transmission over noisy channels

Cai

Chen

2001

IEEE Trans. Circuits Syst. Video Technol.

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“…For example, [29] designed an image coder which uses TCQ to encode the coefficients resulting from the application of the 2-D DCT. For encoding the "Lenna" image, it is shown that peak signal-to-noise ratios (PSNR) of 39.33, 35.97, and 32.49 dB are obtained at encoding rates of 1.0, 0.5, and 0.25 bits/pixel, respectively.…”

Section: Introductionmentioning

confidence: 99%

<title>Hyperspectral image compression using 3D discrete cosine transform and entropy-constrained trellis-coded quantization</title>

1994

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A system is presented for compression of hyperspectral imagery which utifizes treffis coded quantization (TCQ). Specifically, TCQ is ued to encode transform coefficients resulting from the application of an 8 x 8 x 8 discrete cosine transform. Side information and rate allocation strategies are discussed. Entropy-constrained codebooks are designed using a modified version of the generalized Lloyd algorithm. This entropy constrained system achieves a compression ratio of greater than 70:1 with an average PSNR of the coded hyperspectral sequence exceeding 40.5 dB.

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“…Varying levels of compression can be achieved by using variable quantization of these coefficients. Other compression algorithms, such as improved quantization of the DCT [2] and wavelet transform compression [3], are much superior both visually and in terms of mean square error, but are not yet image processing standards like JPEG.…”

Section: Jpeg Image Compressionmentioning

confidence: 99%

The effect of lossy image compression on image classification

Paola

Schowengerdt

1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Application

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We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network.

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Transform coding of monochrome and color images using trellis coded quantization

Cited by 28 publications

References 27 publications

Uniform threshold TCQ with block classification for image transmission over noisy channels

Uniform threshold TCQ with block classification for image transmission over noisy channels

<title>Hyperspectral image compression using 3D discrete cosine transform and entropy-constrained trellis-coded quantization</title>

The effect of lossy image compression on image classification

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