Yusuke Monobe

Dynamic range compression preserving local image contrast for digital video camera

This paper presents a novel dynamic range compression method for digital video cameras. The proposed algorithm is designed to preserve a local image contrast based on a luminance ratio of a pixel to its local surround in attention. In general, digital video cameras are required to maintain the shadow to middle range luminance that includes a main object such as a facial area. According to this requirement, most digital video cameras have adopted a knee curve as a dynamic range compression function that strongly compresses only the highlight range over the knee point. But this method has the weakness that severely lowers a highlight contrast. To overcome this weakness, the proposed algorithm automatically and adaptively enhances the local image contrast in the highlight regions. As a result, the highlight area such as blue sky is realistically reproduced without lowering the input luminance in the shadow to middle range.

High dynamic range compression for digital video camera using local contrast enhancement

This work presents a high dynamic range compression technique that realistically reproduces the images captured by the digital video cameras. This technique adaptively enhances the local contrast of the highlight regions without lowering the luminance of the facial areas.

Display. Special Contribution from Asia Display/IDW'01. Enhancement of JPEG Coded Images Using a Region-based Algorithm.

We describe an improved post-processing algorithm that reduces the blocking and ringing artifacts caused by quantization noise of the DCT coefficients in block-coding techniques such as JPEG without excessively blurring. It is a region-based algorithm that is iteratively applied based on projection onto convex sets. It first segments the decoded image into three regions : the region where blocking artifacts are strongly presented, the region consisting of edge pixels, and the region where ringing artifacts are presented. A process for reducing quantization noise suitable for each region is then calculated. Finally, the DCT coefficients are modified in accordance with the quantization constraint. Some experimental results demonstrate that this algorithm effectively removes the blocking and ringing artifacts in each region without excessively blurring.