1. JPEG DCT Quantization FDCT of 8x8 blocks.
Order in increasing spatial frequency (zigzag)
Low frequencies have more shape information, get finer quantization.
High’s often very small so go to zero after quantizing
If source has 8-bit entries ( s in [-27, 27-1), can show that quantized DCT needs at most 11 bits (c in [-210, 210-1])
See Wallace paper, p 12.
Note high frequency contributions small.

2. JPEG DCT Quantization Quantize with single 64x64 table of divisors
Quantization table can be in file or reference to standard
Standard quantizer based on JND.
Note can have one quantizer table for each image component
See Wallace p 12.

3. JPEG DCT Intermediate Entropy Coding
Variable length code (Huffman):
High occurrence symbols coded with fewer bits
Intermediate code: symbol pairs
symbol-1 chosen from table of symbols si,j
i is run length of zeros preceding quantized dct amplitude,
j is length of huffman coding of the dct amplitude
i = 0…15, j= 1…10, and s0,0=‘EOB’ s15,0 = ‘ZRL’
symbol-2: Huffman encoding of dct amplitude
Finally, these 162 symbols are Huffman encoded.

4. JPEG components
Y = 0.299R G B Cb = R G + 0.5B Cr = 0.5R G B
Optionally subsample Cb, Cr
replace each pixel pair with its average. Not much loss of fidelity. Reduce data by 1/2*1/3+1/2*1/3 = 1/3
More shape info in achromatic than chromatic components. (Color vision poor at localization).

5. JPEG goodies
Progressive mode - multiple scans, e.g. increasing spatial frequency so decoding gives shapes then detail
Hierarchical encoding - multiple resolutions
Lossless coding mode
JFIF:
User embedded data
more than 3 components possible?

6. Huffman Encoding 1
00 s1
01 s2
11 s3
100 s4 10
101
1011 s6
1010 s5

7. Huffman Encoding
Traverse from root to leaf, then repeat:
s3 s5 s3 s2 s4 1
00 s1
01 s2
11 s3
100 s4 10
101
1011 s6
1010 s5