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4:2:0 chroma subsampling keeps the full luma grid while halving each colour-difference component's sampling rate horizontally and vertically. For even width $W$ and height $H$, it retains $WH$ Y samples and $WH/4$ samples each of Cb and Cr.^[ref] The zero in the notation does not mean that one colour-difference component has disappeared.

For an original six-by-four eight-bit image, the active payload iscompared with 72 bytes for 4:4:4 chroma sampling and 48 bytes for 4:2:2 chroma subsampling. Thus the 4:2:0 payload is half the 4:4:4 payload and three quarters of the 4:2:2 payload under these declared assumptions. It averages 12 bits per original pixel, without row padding or alpha.

Both colour components have fewer samples, so chroma reconstruction must estimate values at additional positions before a full-resolution colour conversion. It cannot generally reverse information discarded during downsampling. The chroma sample location also matters: identical counts can correspond to different spatial offsets.

NV12 stores the reduced components as interleaved Cb/Cr pairs after a luma plane; NV21 reverses each pair. A fully planar pixel format can keep the two colour-difference arrays separate. All can carry 4:2:0 sampling, so plane count and pair order must be specified in addition to the sampling ratio.

4:2:2 chroma subsampling keeps the full luma sample count while sampling each colour-difference component at half the horizontal rate and the full vertical rate. Thus a horizontal group with four Y samples has two Cb and two Cr samples.^[ref]

For an original sample-count example, let the grid be six positions wide and three rows high. It contains 18 Y samples and nine samples of each colour-difference component. At eight bits per sample, its active payload isThe corresponding 4:4:4 chroma sampling payload is 54 bytes. The reduction is one third, or from 24 to 16 bits per original pixel. These counts omit padding and any alpha channel; they describe a sampling grid rather than every storage API's alignment constraints.

Horizontal fine colour detail can be lost even though the vertical sample count is unchanged. If a colour-difference row alternates between codes 40 and 160 at every luma position, pair averaging gives three samples of 100 on the stipulated six-position row. Interpolating from those averages cannot identify the original alternation. A low-pass filter and an explicit chroma sample location describe how the reduction is performed; the ratio alone does not specify either.

For a grid whose colour samples share the luma row coordinates, chroma reconstruction needs to estimate missing horizontal positions without inventing a missing vertical row. A later conversion to 4:2:0 chroma subsampling additionally reduces the colour sampling vertically. Rearranging the same 4:2:2 samples between packed pixel formats and planar pixel formats changes layout without making that further sampling reduction.

Chroma subsampling represents colour-difference components on a coarser spatial grid than luma. In YCbCr video, 4:4:4 chroma sampling keeps full sample counts for every component; 4:2:2 chroma subsampling halves the colour-difference count horizontally; 4:2:0 chroma subsampling halves it horizontally and vertically.^[ref]

For an original eight-bit $4\times 2$ image with no padding or alpha, full sampling uses eight samples in each of three components: 24 bytes. With horizontal halving, the count becomes $8+4+4=16$ bytes. Halving in both directions gives $8+2+2=12$ bytes. These count active samples, not the allocation overhead of a particular pixel format.

The reduction loses information unless the discarded variations are otherwise constrained. For a stipulated pair of neighbouring samples with constant luma, let one colour-difference channel have values $+0.1$ and $-0.1$. Averaging the pair and retaining only the average stores zero. Reconstructing both positions as zero cannot recover which one was originally positive. Fine colour variation can therefore disappear even though both luma samples remain intact.

A low-pass filter before reducing the sampling rate controls aliasing; merely dropping alternate samples need not. Later chroma reconstruction estimates the missing positions. It must respect chroma sample location: equal sample counts do not determine where those samples lie relative to the luma grid. The Microsoft primary guide explicitly distinguishes different 4:2:0 grids and discusses interpolation during format conversion.^[ref]

Storage organization is independent of this sampling ratio. NV12 interleaves the two reduced components in a second plane, while a fully planar pixel format can store them in separate planes. Rearranging the same samples changes layout, not sampling resolution.

Chroma upsampling increases the spatial sample count of the colour-difference components after chroma subsampling. For example, converting 4:2:0 chroma subsampling to 4:2:2 chroma subsampling doubles the vertical colour sample count, while conversion to 4:4:4 chroma sampling also doubles it horizontally.^[ref] The added values are estimates, not newly measured image detail.

An original one-dimensional derivation separates increasing the grid density from interpolation. Start with samples $c_k$ and insert zero-valued placeholders:Apply the chroma interpolation filterIt follows that $y_{2k}=c_k$ and $y_{2k+1}=(c_k+c_{k+1})/2$. Thus this stipulated filter preserves the original positions and fills the intervening positions by linear interpolation.

The scaling is essential. For a constant input $c_k=128$, the correctly scaled result is128 at every output position. Using the ordinary three-tap averaging coefficients $(1/4,1/2,1/4)$ on the zero-inserted sequence would instead give64 everywhere. Those coefficients are suitable for a different operation; their unit sum alone does not preserve a constant after zero insertion.

The derivation assumes the retained samples occupy the even positions of the output grid and ignores image boundaries. Centered chroma can require a different alignment, and finite images require an edge rule. The Microsoft example specifies progressive scanning, interpolation and edge handling explicitly.^[ref] Upsampling quality therefore depends on chroma sample location, filter choice and boundary treatment, rather than only on output dimensions.