Interframe Cosine Transform Image Coding

Roese, John A.; Pratt, William K.; Robinson, Guner S.

doi:10.1109/tcom.1977.1093761

Cited by 106 publications

(17 citation statements)

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“…Two dimensional DCT has previously been used for feature extraction from spatial data in applications such as image compression [20], face recognition [7] and image retrieval [6]. Similarly, the 3D-DCT has been used to simultaneously exploit the spatiotemporal information contained in the correlated video frames in applications such as video coding [3,15] and visual tracking [10]. However, we use 3D-DCT, for the first time, for information compaction and features extraction from hyperspectral data.…”

Section: D-dct Based Feature Extractionmentioning

confidence: 99%

Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares

Uzair¹,

Mahmood²,

Mian³

2013

Procedings of the British Machine Vision Conference 2013

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Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, compact and discriminative feature extraction from high dimensional hyperspectral image cubes is a challenging task. We propose a spatio-spectral feature extraction method based on the 3D Discrete Cosine Transform (3D-DCT). The 3D-DCT optimally compacts information in the low frequency coefficients. Therefore, we represent each hyperspectral facial cube by a small number of low frequency DCT coefficients and formulate Partial Least Square (PLS) regression for accurate classification. The proposed algorithm is evaluated on three standard hyperspectral face databases. Experimental results show that the proposed algorithm outperforms five current state of the art hyperspectral face recognition algorithms by a significant margin.

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Section: D-dct Based Feature Extractionmentioning

confidence: 99%

Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares

Uzair¹,

Mahmood²,

Mian³

2013

Procedings of the British Machine Vision Conference 2013

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“…Although the 3eparable model of (A-i) has been used for data compression [67,68,87], for reasons discussed in Appendix A, nonseparable models are preferable in many cases. Therefore,the nonseparable models described by (A-2), (A-3), (A-4), (A-5) and others have been used for data compression [49,53,67,68,75] with better results than the separable model. The most commonly used transforms in these studies are the Cosine and the Hadamard.…”

Section: Experimental Results and Distortion-rate Functionsmentioning

confidence: 99%

“…But we do need the transform domain variances to design a coder without resorting to trial and error. In have also been investigated in [67,68]. These are extensions of the intraframe hybrid coding described in [22].…”

Section: S-mentioning

confidence: 99%

Interframe Adaptive Data Compression Techniques for Images.

Jain¹,

Jain²

1979

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“…First, a quantization table, described for example by JPEG, 2 can be used individually for each time-dependent 8 × 8 block 4 or can be used to construct a whole 8 × 8 × 8 quantization volume. 5 Second, the unique quantization volume can be constructed, for example, based on human visual acuity 6 or based on the statistical properties of coefficients, 7,8 like variance distribution. In addition, adaptive methods minimizing the quantization error 6 have been proposed to construct a quantization volume.…”

Section: Quantizationmentioning

confidence: 99%

Reducing computational complexity of three-dimensional discrete cosine transform in video coding process

2007

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Low complexity video coding schemes are aimed to provide video encoding services also for devices with restricted computational power. Video coding process based on the three-dimensional discrete cosine transform (3D DCT) can offer a low complexity video encoder by omitting the computationally demanding motion estimation operation. In this coding scheme, extended fast transform is also used, instead of the motion estimation, to decorrelate the temporal dimension of video data. Typically, the most complex part of the 3D DCT based coding process is the three-dimensional transform. In this paper, we demonstrate methods that can be used in lossy coding process to reduce the number of one-dimensional transforms required to complete the full 3D DCT or its inverse operation. Because unnecessary computations can be omitted, fewer operations are required to complete the transform. Results include the obtained computational savings for standard video test sequences. The savings are reported in terms of computational operations. Generally, the reduced number of computational operations also implies longer battery lifetime for portable devices.

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Interframe Cosine Transform Image Coding

Cited by 106 publications

References 0 publications

Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares

Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares

Interframe Adaptive Data Compression Techniques for Images.

Reducing computational complexity of three-dimensional discrete cosine transform in video coding process

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