In this letter, we propose a novel contrast enhancement algorithm for optical remote sensing satellite and aerial images. The algorithm is based on multiscale retinex (MSR). Unlike the MSR, however, we first decompose retinex outputs into nearly nonoverlapping spectral subbands. We then apply a space-varying subband gain to each subband-decomposed retinex output by considering its subband characteristic so that the image contrast can be effectively enhanced. In order to improve details in both shadows and highlights, we propose a new hybrid intensity transfer function based on logarithmic functions. In addition, we make the degree of enhancement of details adjustable by introducing a detail adjustment function based on a local standard deviation map of the original image. Experimental results clearly demonstrate that the proposed algorithm effectively distinguishes details in the whole image automatically and greatly enhances the overall contrast in various images.

In this paper, we propose a novel image enhancement algorithm by improving the standard multiscale retinex (MSR) algorithm. A modified retinex output using a combination of two logarithmic functions enhances the details in highlights as well as the details in shadows. Sub-band decomposition of the modified retinex output makes the individual gain control of spectral bands possible, so that we can enhance image details according to band characteristics. Due to the effective sub-band based gain control, the final enhanced image can be automatically obtained without a user-assisted "canonical" gain/offset control. Experimental results clearly demonstrate that the proposed algorithm effectively distinguishes the details in both highlights and shadows and greatly enhances the overall contrast while maintaining a natural scene.

We propose a new hierarchical motion estimator architecture that supports the advanced prediction mode of recent low bit-rate video coders such as H.263 and MPEG-4. In the proposed VLSI architecture, a basic searching unit (BSU) is commonly utilized for all hierarchical levels to make a systematic and small sized motion estimator. Also, since the memory bank of the proposed architecture provides a scheduled data flow for calculating the 8×8 block-based sum of absolute difference (SAD), both a macroblock-based motion vector (MV) and four block-based MVs per macroblock can be simultaneously obtained for the advanced prediction mode. Even with its small size of implementation, the proposed motion estimator provides similar coding performance as that of the full search block matching algorithm (FSBMA).

This paper presents a method to remove blocking artifacts in low bit-rate block-based video coding. The proposed algorithm has two separate filtering modes, which are selected by pixel behavior around the block boundary. In each mode, proper one-dimensional filtering operations are performed across the block boundary along the horizontal and vertical directions, respectively. In the first mode, corresponding to flat regions, a strong filter is applied inside the block as well as on the block boundary because the flat regions are more sensitive to the human visual system (HVS) and the artifacts propagated from the previous frame due to motion compensation are distributed inside the block. In the second mode, corresponding to other regions, a sophisticated smoothing filter which is based on the frequency information around block boundaries, is used to reduce blocking artifacts adaptively without introducing undesired blur. Even though the proposed deblocking filter is quite simple, it improves both subjective and objective image quality for various image features.

Motion vector coding efficiency is becoming an important issue in low bitrate video coding because of its increasing relative bit portion. This work presents a new motion vector coding technique based on minimum bitrate prediction. In the proposed scheme, a predicted motion vector is chosen from the three causal neighboring motion vectors so that it can produce a minimum bitrate in motion vector difference coding. Then the prediction error, or motion vector difference (MVD), and the mode information (MODE) for determining the predicted motion vector at a decoder are coded and transmitted in order. Sending bits for the MVD ahead of bits for the MODE, the scheme can minimize the bit amount for the MODE by taking advantage of the fact that the minimum bitrate predictor is used for motion vector prediction. Adaptively combining this minimum bitrate prediction scheme with the conventional model-based prediction scheme, more efficient motion vector coding can be achieved. The proposed scheme improves the coding efficiency noticeably for various video sequences.